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YADAVA, Amina Kumar Singh, 1921- PREDICTION OF GIVING TO COMMUNITY FUND RAISING FEDERATIONS: USE OF MULTIPLE LINEAR REGRESSION, CONFIGURATION, GLUECK AND BURGESS APPROACHES.
The Ohio State University, Ph.D., 1962 Sociology, public welfare University Microfilms, Inc., Ann Arbor, Michigan PREDICTION OF GIVING TO COMMUNITY FUND-RAISING
FEDERATIONS: .
Use of Multiple Linear Regression, Configuration,
Glueck and Burgess Approaches
DISSERTATION
Presented in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy in the Graduate School of The Ohio State University
By
Amina Kumar Singh Yadava, B.Sc„, L. L.B ., M .A., A.M.
The Ohio State University 1962
Approved by
A d v iser School of Social Work ACKNOWLEDGMENTS
It would have been impossible to produce this dissertation in the present form without the wholehearted cooperation of a number of persons. The writer takes this opportunity to acknow ledge his appreciation of their valuable assistance.
The writer is extremely indebted to his major adviser, Pro fessor M erriss Cornell, who suggested many of the ideas incor porated in this study. He gave generously of his time from the initial stages of the research to its completion. It is hard to find
suitable words to express adequately this w riter's gratitude to h im .
Professors Everett C. Shimp, Director, and Russell W.
Leedy, School of Social Work, The Ohio State University, were both most helpful. Their suggestions were invaluable.
The writer also acknowledges his appreciation to Dr. Ray
mond F. Sletto, Chairman, Department of Sociology, The Ohio
State University, for his help in selecting the topic and for his
thoughtful guidance. Dr. Robert P. Bullock, Dr. Halliman
Winsborough and Dr. C. T. Jonassen of the same department
gave their time and stimulating ideas ungrudgingly.
Dr. Everett M. Rogers, Dr. Wade H. Andrews, and Dr.
Arthur R. Mangus of the Rural Sociology Department were also of much help in the early stages of this study. They gave many useful suggestions regarding indices of community variables.
The writer is very thankful to the staff of the Research
Department, United Community Funds and Councils of America,
Incorporated, especially to Mr. Kenneth Wood and Mr. Arthur
Jette,. Director and Associate Director respectively, for their cooperation in making all possible facilities and records available.
The staff rendered a great help in preparing a questionnaire and mailing it to the executives of the respective fund-raising federations.
The w riter would also like to take this opportunity to acknow ledge his appreciation of the cooperation given by the executives of various fund-raising federations by providing data included in the questionnaire.
Mr. Robert C. Hicks, Executive Director, United Appeal of Franklin County and Mr. Dean F. Luse, Research Director,
United Community Council, Inc., were very kind in discussing some of the problems related to the study. They offered a number of very useful suggestions.
The writer also expresses his gratefulness to the staff of the Numerical Computation Laboratory, The Ohio State University, for extending every possible help in the processing of data by com
iii puters. Without this help it would have been humanly impossible to finish all the computation, even in a period of ten years. Dr.
Roy F. Reeves, Director and Mr. Dickson H. Call, Graduate
Research Associate, were extremely helpful. Mr. Omar S. Goode,
Research Associate, Bureau of Business Research, also offered considerable help in reproducing cards and in the sorting process.
In the end, the writer feels most grateful to Mrs. Esther
Whaley, Editor and Mrs. Melba Griffin, of the Graduate School for their extreme kindness in extending every possible help. CONTENTS
ACKNOWLEDGMENTS...... ii TA BLES ...... v i FIGU RES ...... xiv M APS ...... xiv
CHAPTER
I. INTRODUCTION ...... 1
II. REVIEW OF THE LITERATURE ...... 21
III. RESEARCH METHODOLOGY ...... 56
IV. RESEARCH FINDINGS AND VALIDATION . 134
V. SUMMARY AND CONCLUSIONS...... 191
APPENDIXES
A. Names of the Communities Included in the Study, by State ...... 195
B. Operational Definitions of Variables Used # 200
C. Sources of Data ...... 205
D. Questionnaire ...... 207
BIBLIOGRAPHY ...... 209
AUTOBIOGRAPHY ...... 213
v TABLES
T ab le P ag e
1 Total Number of Fund-Raising Federations and of Those Included in the Study ...... 60
2 Geographical Locations of Fund-Raising Federations Included in the Study ...... 60
3 Number of Communities and Probability Values Associated with Categories of Median Family Income, 1951 ...... 73
4 Predictive Configurations Associated with Low Category, Size of Sub sample, and Probability Values Based Upon Contiguous Categories, 1951...... 82
5 Predictive Configurations Associated with High Category, Size of Subsample, and Probability Values Based upon Contiguous Categories, 1951 ...... 83
6 Predictive Configurations Associated with Low Category, Size of Subsample, and Probability Values Based upon Widespread Categories, 1951 ...... 84
7 Predictive Configurations Associated with High Category, Size of Subsample, and Probability Values Based upon Widespread Categories, 1951 . ... .85
8 Number, Population Size of Communities and Specification of Independent Variables Included in Instruments Based on Multiple Linear Regression Method, 1951 ...... 86
v i TABLES
T a b le P a g e
9 Coefficients of Regression, Multiple Correlation, Multiple Determination for Variables in Regression Equation for Size 25,000 and over, 1951 90
10 Coefficients of Regression, Multiple Correlation, Multiple Determination for Variables, in Regression Equation for Size 25, 000-49, 999, 1951 ...... 91
11 Coefficients of Regression, Multiple Correlation, Multiple Determination for Variables in Regression Equation for Size 50,000-99,999, 1951 ...... 92
12 Coefficients of Regression, Multiple Correlation, Multiple Determination for Variables in Regression Equation for Size 100, 000-.249, 999, 1951 ...... 93
13 Coefficients of Regression, Multiple Correlation, Multiple Determination for Variables in Regression Equation for Size 250, 000 and over, 1951 94
14 Coefficients of Regression, Multiple Correlation, Multiple Determination for Variables in Regression Equation for Size 25,000 and over, 1951 95
15 Coefficients of Regression, Multiple Correlation, Multiple Determination for Variables in Regression Equation for Size 25,000-49, 999, 1951 96
v ii TABLES
T a b le P a g e
16 Coefficients of Regression, Multiple Correlation, Multiple Determination for Variables in Regression Equation for Size 50, 000-99, 999, 1951...... 97
17 Coefficients of Regression, Multiple Correlation, Multiple Determination for Size 100,000 and over, 1951 ...... 98
18 Coefficients of Regression, Multiple Correlation, Multiple Determination for Variables in Regression Equation for Size 50,000 and over, 1951 99
19 Coefficients of Regression, Multiple Correlation, Multiple Determination for Variables in Regression Equation for Size 50, 000-99, 999, 1951 100
20 Coefficients of Regression, Multiple Correlation, Multiple Determination for Variables in Regression Equation for Size 100, 000 and over, 1951 ...... 101
21 Variables Progressively Included and Resulting Coefficients of Multiple Correlation and Multiple Determination for Instrument Selected for Size 25, GOO- 49, 999, 1951 ...... 102
22 Variables Progressively Included and Resulting Coefficients of Multiple Correlation and Multiple Determination for Instrument Selected for Size 50, GOO- 99, 999, 1951 ...... 103
v iii TABLES
T a b le P a g e
23 Variables Progressively Included and Resulting Coefficients of Multiple Correlation and Multiple Determination for Instrument Selected for Size 100,000 and over, 1951 ...104
24 Critical Categories and Probability Values of Factors Included in Instrument Based on Burgess method, 1951 ...... 106
25 Prediction Scores from Burgess Instrument and Per Capita Giving, 1951 ...... 107
26 Dichotomy of Prediction Scores from Burgess Instrument, and Per Capita Giving, 1951 107
27 Values of Chi-square, Coefficients of Contingency of Variables Highly Associated with Criterion, 1951 ...... ,109
28 Variables Included in Instrument based upon Glueck method and Categories with Highest and Lowest Scores, 1951 ...... ,110
29 Prediction Scores from Glueck Instru ment and Per Capita Giving, 1951 Ill
30 Dichotomy of Prediction Scores from Glueck Instrument and Per Capita Giving, 1951 Ill
31 Predictive Configurations Associated with High and Low Categories, Size of Sub sample and Probability Values Based upon Contiguous Categories, 1961 117
ix TABLES
Tables Page
32 Variables Included in Instrument Based on Glueck method and Categories with Highest and Lowest Scores, 1961 ...... 118
33 Prediction Scores from Glueck Instrument, 1961 119
34 Coefficients of Regression, Multiple Correlation, Multiple Determination for Variables in Regression Equation for Size 25,000 and over, 1961 ...... 1 2 0
35 Coefficients of Regression, Multiple Correlation, Multiple Determination for Variables in Regression Equation for Size 25,000-99, 999, 1961...... 121
36 Coefficients of Regression, Multiple Correlation, Multiple Determination for Variables in Regression Equation for Size 100,000 and over, 1961 ...... 122
37 Variables Progressively Included and Resulting Coefficients of Multiple Cor relation and Multiple Determination for Instrument for Size 25,000 and over, 1961 ..... 123
38 Variables Progressively Included and Resulting Coefficients of Multiple Correlation and Multiple Determination for Instrument for Size 25,000-99,999, 1961 124
39 Variables Progressively Included and Resulting Coefficients of Multiple Correla- ' tion and Multiple Determination for Instrument for Size 100,000 and over, 1961 .... 125
40 Means and Standard Deviation of Selected Variables used in Construction of Instruments . . 130
x TABLES
T a b le P a g e
41 Z e ro o rd e r c o rre la tio n s of Independent Variables Used in the Study, 1950 136
42 Zero order correlations of Independent Variables Used in the Study, I960 ...... 138
43 Correlation Matrix of all Variables Used for Constructing Instruments Based upon 1950 Data ...... 140
44 Correlation Matrix of All Variables Used for Constructing Instruments Based upon I960 Data ...... 145
45 Outcome Predicted for 1951 Sample from Configuration Instrument Based upon 1950 Data: Contiguous Categories ...... 149
46 Outcome Predicted for 1951 Sample from Configuration Instrument Based upon 1950 Data: Widespread Categories ...... 150
47 Outcome Predicted for 1961 Sample I from Configuration Instrument Based upon 1950 Data: Contiguous Categories...... 151
48 Outcome Predicted for 1961 Sample I from Configuration Instrument Based upon 1950 Data: Widespread C ategories ...... 152
49 Outcome Predicted for 1961 Sample I from Configuration Instrument Based upon I960 Data: Contiguous C ategories...... 153
x i TABLES
T ab le P a g e
50 Outcome Predicted for 1961 Sample II from Configuration Instruments Based upon 1950 and I960 Data: Contiguous Categories ...... 154
51 Outcome Predicted for 1951 Sample from Regression Equations Based upon 1950 Data . . . .156
52 Outcome Predicted for 1961 Sample I from Regression Equations Based upon 1950 Data .... 157
53 Outcome Predicted for 1961 Sample I from Regression Equations Based upon I960 Data .... 158
54 Outcome Predicted for 1961 Sample II from Regression Equations Based upon 1950 and 1960 D ata ...... 159
55 Outcome Predicted for 1951 Sample from Burgess Instrument Based upon 1950 Data ..... 160
56 Outcome Predicted for 1961 Sample I from Burgess Instrument Based upon 1950 Data ..... 161
57 Outcome Predicted for 1951 Sample from Glueck Instrument Based upon 1950 D ata ...... 162
58 Outcome Predicted for 1961 Sample I from Glueck Instrument Based upon 1950 D ata ...... 163
59 Outcome Predicted for 1961 Sample I from Glueck Instrument Based upon I960 Data ...... 164
60 Outcome Predicted for 1961 Sample II from Glueck Instruments Based upon 1950 and I960 Data ...... 165
x ii TABLES
Tables Page
61 Outcome Predicted for 1961 Sample II for Instruments Constructed According to Multiple Linear Regression and Glueck methods, by Name and Size of Comm unity ...... 166
62 Predictive Accuracy of Alternative Prediction Methods, Total Sam ple ...... 170
63 Predictive Accuracy of Alternative Prediction Methods, Borderline Communities Excluded ...... 171
64 Proportion of Communities in each Criterion Category Predicted Correctly by Alternative Prediction M ethods ...... 173
65 Predictive Efficiency of Alternative Prediction Methods ...... 174
66 Level of Significance of Critical Ratios Representing Differences in Efficiency between Alternative Prediction Methods ...... 175
67 Relative Predictive Stability of Alternative Prediction Methods ...... 176
x iii FIGURES
Figure Page
1 Factors Included in the Two Predictive Instruments Based on Predictive Configuration Method ...... 72
2 Probability Values of Predictive Con figurations Involving all the 350 Communities, , , 74
3 Probability Values of Predictive Con figurations Involving Communities . having Median Family Income between Third and Fifth Decile ...... 75
4 Probability Values of Predictive Configurations Involving Communities having Median Family Income above Fifth Decile ...... 76
5 Probability Values of Predictive Configurations Involving 280 Communities: Widespread Categories ...... 77
6 Probability Values of Predictive Configurations Involving Communities having Median Family Income above Sixth Decile: Widespread Categories ...... 78
7 Probability Values of Predictive Configura tions Involving Communities having Median Family Income between Third and Sixth Deciles: Widespread C ategories ...... 79
8 Probability Values of Predictive Config urations Involving 100 Communities, 1961 ...... 112
x iv FIGURES
Figure Page
9 Probability Values of Predictive Configurations Involving Communities having Median Family Income above Third Decile, 1961 113
MAPS
M ap P age
1 Communities Included in 1951 Sam ple ...... 61
2 Communities Included in 1961 Sample I and I I ...... 62
xv C H A PTER I
INTRODUCTION
Historical Background
As a result of the rapid growth and development of industry and commerce in this country during the nineteenth century there was a phenomenal increase both in the number and size of cities.
This was bound to have far-reaching consequences on the socio economic life of the people. On the one hand, there was widespread poverty, accompanied by various social and economic problems, but at the same time, on the contrary, there was accumulation of vast stores of wealth, which could be utilized to attack the problems of misery, for raising economic production and standard of living, and for developing knowledge of better methods of meeting social problems and of organizing social services.
Because of the widespread nature and immense magnitude of the problems, the old benevolent and paternalistic approach of helping one's neighbor was bound to be utterly inadequate. It was realized, sooner or later, that the solution of such problems demands the development of more organized groups or associar tions to aid and mitigate the social and economic ills. The transition from the earlier individualistic method of meeting social ills to organized social services has been very aptly described by W. J. Norton:
The nation was a nation of individual prosperity hunters, controlled by the hardy ideals of the early settlers, which had been welded into a social tradition by hardy successors, Self»sufficiency was a habit. Individual initiative was a fetish, Americans hated failure and were loath to recognize that anyone must of necessity fail. The fear of the pauperizing influence of charity and a general m istrust of group control was pronounced. There was a grudging dread of the growth of the powers of the state and of organized groups. Concessions to group welfare had literally to be wrenched from this gospel of free initiative. Yet it was done iych by inch and bit by bit over a series of years.
In 1877 Buffalo took a long stride in the direction of bringing
some order out of chaos in the field of social services with the
creation of its charity organization society, which was modeled
after the London Society, The idea spread rapidly to other cities
and within four years twenty cities had organized such societies.
In later years, however, its original objectives, that is, of brings
ing coordination between the various social welfare services,
were taken over by the Council of Social Agencies.
*W. J, Norton, The Cooperative Movement in Social Work (New York: Macmillan Company, 1927), pp. 7-8.
2 The first two councils of social agencies in this country 2 were organized in Milwaukee and Pittsburgh in 1909. In the following years a number of other cities followed suit. The move ment gained further impetus during World War I. Today, a large number of cities, particularly those having populations over
50,000, have a social welfare council. In some communities, the social planning function is carried out through community chests or fund-raising federations.
Prior to the economic depression of the 1930's, major re sponsibility for social welfare was shouldered by voluntary organizations. During the past two or three decades, however, the government has been assuming increasingly greater responsi bility in this sphere. Broadly speaking, governmental social welfare agencies are responsible for meeting, to a greater or lesser extent, basic economic, health, and educational needs; in some respects, for the whole population and in others, for only certain classes of the needy. Calculations of Andrews indicate that governmental expenditure is now about nine times the amount of voluntary giving for purposes which a generation or two ago 3 would have been deemed to lie within the field of private charity.
Yesterday and Today with Community Chests (New York: Community Chests and Councils, Inc., 1937), p. 7. (pamphlet.) F. Emerson Andrews, Philanthropic Giving (New York: Russell Sage Foundation, 1950), p. 43. However, in spite of the fact that almost all the expenditure on public assistance is now met from tax funds, the appeals for funds from voluntary agencies are still on the increase. This is es sentially because of two reasons: first, it is generally accepted in this country that social welfare services prim arily concerned with character building and personality or social adjustment prob lems should be sponsored, preferably, by voluntary agencies.
Also, the role of voluntary agencies in the spheres of experimental tion or research, and in filling in gaps and covering additional needs not now met by government, is generally recognized. Second, there have been increasing efforts during the past two or three decades to improve the quality of service through providing better qualified personnel and more adequate facilities. No wonder the quality of staff and services in voluntary agencies is sometimes superior to that in some of the government-sponsored agencies.
The relations between public and voluntary agencies are still in a state of flux. Andrews has very aptly observed that the extent to which voluntary agencies survive the trend toward wider governmental services will depend not only upon judgment as to relative efficiency and usefulness but upon their ability to collect 4 fu n d s,
~ ^F. Emerson Andrews, Corporation Giving (New York: Russell Sage Foundation, 1952), p. 176. Philanthropy is a very important factor in American life.
More than 500, 000 gift supported institutions and organizations now serve the American people. These have assets in property and endowments of 45 billion dollars or more. The people of the United
States are giving at the rate of 6 billion dollars a year for the 5 support of religious, educational and philanthropic institutions.
Prior to the growth of federated fund-raising during the first decade of the present century, each voluntary agency had to launch its own appeal for funds. This involved much waste in term s of time and energy of the agency workers, both voluntary and profes
sional, as well as expenditure incurred in making collections.
The earliest experiment in federated fund«*raising in this country was made in Denver, Colorado, which in 1887 established a
community-wide institution, named the Associated Charities of
Denver, for collecting funds for the support of 23 local agencies.^
In 1913 Cleveland took a long stride in this direction, when it
organized the Federation of Charities and Philanthropy, considered 7 as the first real community chest.
^David M. Church, Philanthropic Fund Raising as a Profession (Cambridge, M assachusetts: Bellman Publishing Company, 1957), p . 8.
^Norton, op. cit., p. 51. 7Andrews, op. cit., p. 138. During World War I, a large number of communities were induced to organize war chests for joint solicitation of funds because of the multiplication of appeals of all sorts. This idea of concentrating campaigns for funds into one joint effort was so widely accepted that it was bound to be carried over into postwar and local service endeavors. In 1918 the American Association for Community Organization (later known as Community Chests and Councils of America, Incorporated, and now named as the
United Community Funds and Councils of America, Incorporated) was formed as a national association of the chests. This organi zation has been responsible for providing nationwide leadership in the fields of social welfare planning and federated fund-raising.
In collaboration with The Ohio State University, it established,
in 1922, a training center for executives of Community Chests and 8 C o u n cils.
It might be in order here to trace, briefly, the growth of
the federated fund-raising movement. In 1920 there were only
39 chests, but by 1925, there were 240, and by 196l, the number
had risen to 2,083. There is hardly any community today with a
8 Arthur Dunham, Community Welfare Organization (New York: Thomas Y. Crowell Company), p. 76.
6 population of 50, 000 or more that does not have a fund-raising federation. It is now estimated that the areas with community
chests or united funds have a total population of 122 million, that is, about 68 per cent of the United States citizens are exposed 9 to federated giving. In terms of the total amount raised, there
has been a phenomenal increase from 20 million dollars in 1920
to 447 million in I960. ^
Although there are several forms of fund-raising federations,
the best known are the community chests and the united funds,
and it is only with regard to these that this study is concerned.
Some examples of other forms of fund-raising federations are
The United Hospital Fund, United Negro College Fund, Catholic
Charities, United Jewish'Appeal, and Federation of Protestant
Welfare Agencies in New York City. Also, a number of national
organizations, which are increasingly opposed to the idea of a
united fund or a community fund-raising federation and do not par
ticipate with them, are, themselves, examples of fund/raising
organizations.
United Community Funds and Councils of America, thus
defines a community chest:
^1961 Directory, United Community Funds and Councils of America, Incorporated, New York, June 1961, p. vi.
•^I b id . 7 A community chest is a cooperative organization of citizens and welfare agencies. It has two chief functions: (1) It raises funds for its affiliated agencies, through a community-wide appeal and distributes them according to a systematic budget procedure. (In time of war or other emergency it may also raise war relief and emergency service funds.) (2) It promotes cooperative planning, coordination and administration of the community's social welfare, health and recreation services. The direct responsibility for this function may be carried by a community welfare council.
Since the end of World War II, there has been an increasing trend toward the development of united funds and extended federa tion campaigns in order to solve the problem of multiple appeal in local communities. The problem arose because within recent years a number of national agencies, concerned with specialized diseases--infantile paralysis, heart, cancer, and so on--had
come into existence; these carried on their own individual fund
raising campaigns in local communities. In 1949 Detroit took a
lead in solving the problem of multiple appeals by establishing a
United Fund, and soon other communities also followed suit. The
united funds have increased from 106 in 1950 to 1 310 in I960, and
they raised, for the year 1961, more than three-fourths of the
total collected by all federated campaigns.
A united fund is defined as follows. It is:
^ Organizing and Operating a Community Chest (New York, CCC, Bulletin 143, rev. ed., 1952), p. 3.
8 an autonomous, non-profit corporation formed for united community action toward eliminating dupli cate campaign efforts and more adequately financing essential voluntary health and welfare services. It is designed to provide for national as well as local services, and the dominant and immediate purpose is that of bringing about one combined fund-raising campaign to take the place of many separate drives. Which causes are to be combined varies from city to city and is a matter for local negotiation and determination. 12
Many fund»raising federations, in addition to having door-to- door canvassing and special gift committees for approaching large givers individually, now conduct carefully organized industry drives for soliciting pledges, often paid in instalments, from both workers and management. Contributions are also sought from corporations themselves. Corporation contributions to philanthropy have in»
creased enormously during the past two or three decades, largely because of changes brought about in tax structure, which encour-
a.ges gifts to charitable institutions and causes. Thus corporation
contributions to charitable agencies are deductible for tax purposes 13 up to 5 per cent of corporate net income.
Although federated financing has made tremendous headway
and has been very widely accepted, there is still some controversy
^Organizing a United Fund (New York, CCC, Bulletin 165, 1953), p. 5.
^Internal Revenue Code, Section 23, v, pp. 271-272.
9 about it. Various arguments have been presented, both for and against the federated financing plan. The protagonists of the plan claim that more money from more people is raised through a joint campaign than through launching separate efforts. The cost of financing social welfare services is reduced, and the agency board members and executives are relieved of the work of raising funds, and thus they are enabled to devote their time and energy to furthering the service programs. Also, because of joint budgeting and accounting, the contributor is assured that his gift 14 is being used effectively.
Those who are opposed to the plan argue that the individual agency loses freedom, since considerable power is centralized in the federation. They contend that the contributor would tend to lose interest in the specific causes or agencies and that it might become real difficult, if not impossible, for new services to come into existence. They also argue that it tends toward the coercion of employees to contribute. 15
During the early years of united fund movement, the
American National Red Cross did not permit its chapters to join
^Dunham, op. cit. , pp. 161-162. the local fund-raising federations. In August, 1951, however, the Red Cross revised its national policy, and allowed its chapters to participate with the federations.^ Today, the Red Cross is 17 included in more than three-fourths of the campaigns.
During 1955-57 the American Cancer Society and American
Heart Association revised their policies and prohibited their local
affiliates in any new participation in federated financing. The
Cancer Society also required that none of its local affiliates shall \
participate in fund-raising federations after 1959.^ Leaders of
some of these national agencies believe that their locals can raise
more funds by remaining outside of financial federations than by
participating in them.
In short, federated financing is still in a transition stage,
although there is no doubt that the movement is definitely gaining
ground. It might be mentioned, parenthetically, that in a few states
such as Michigan and the Carolinas, even state-wide federations
have been developed.
^Andrews, Corporation Giving, op. cit. , p. 170. 17 Dunham, op. cit. , p. 188.
18 American Cancer Society. News release, March 17, 1955. Division circular letter, Memorandum on the New Fund-Raising Policy of the American Cancer Society, November 6, 1957.
11 Statement of the Problem and Its Significance
Setting of the campaign goal has always been a difficult problem. To set a goal successfully we must know not only the agency need but also the campaign potential. The goal is usually a compromise between what is believed to be needed and what it 19 is thought can be raised. The budget committee of a fund-raising federation is usually able to make a reasonably accurate assess ment of the agency needs. It is in the area of estimating campaign potential that much difficulty is encountered. The United Community
Funds and Councils of America has recently developed a method for measuring the fund-raising potential in a community. The method is discussed in the next chapter.
The knowledge of the fund-raising potential of a community does help in estimating, to some extent, the per capita giving to its fund-raising federation. However,. there are so many other significant factors, such as community efforts to raise funds, or the cooperation of corporations, that must be taken into account, if one is to predict the giving with a sufficient degree of accuracy.
Thus, any two communities having similar social welfare needs and the same fund-raising potential may vary considerably in their
giving performances.
^Dunham, op. cit. , p. 169.
12 To the best of this w riter’s knowledge, no predictive type of study has been made so far with regard to any aspect of a com munity, including that of per capita giving, with which this study is mainly concerned. Survey of the literature, and interviews with the staff of the Research Department of the United Community
Funds and Councils of America, revealed that no attempt has yet been made to determine whether or not and if so, to what extent, various characteristics of a community are related to the amount of funds raised in it by the federation. These were some of the main reasons for selecting the present study.
In regard to the significance of the study, it might be stated that the methodology followed or developed in the present study would, hopefully, pave the way for developing instruments for predicting any other form of behavior, outcome or performance of a community. For example, in underdeveloped countries, like
India, where socioeconomic problems exist on mass scale, and where financial as well as personnel resources are limited, it is often necessary when starting a new program to select a few pro mising communities or villages to serve as pilot projects. The aim, most often, is to select those communities where the chances for the success of the program are relatively high so that other communities in the neighborhood may be more willing to try it.
13 The writer feels that by using a similar methodology, as followed or developed in the present study, predictive instruments can be i designed that might make the task of the selection of communities not only more objective but also more accurate.
Also, insofar as the development of prediction is a sine qua non for the advancement and growth of both theory and practice, this study might be helpful in the development of knowledge in the field of community organization, in general, and of federated fund raising, in particular. Further, since the study aims to provide an insight into the various factors in a community that influence the giving, it is hoped that better results might be achieved by a conscious manipulation, on the part of fund-raising federations, of one or more of these factors.
It may also be added that if a sufficiently reliable and valid instrument can be built for the prediction of community performance on a particular criterion, then it might also facilitate a more ob jective and meaningful comparison of the performances of commu nities. It is safe to presume that for those communities, whose actual and predicted performances are significantly sim ilar, the effect of all other factors, save those used in the construction of the instrument, can be ignored. On the contrary, if a community's performance is significantly different from the one predicted, it might be conjectured that the community has some characteristics of "uniqueness, " or that it is in one or more respects "deviant" from the general norm for the country. This might offer some clue for better understanding of the "deviant" characteristics of the community.
Purpose of the Study
The purpose of the study was to determine factors that are related to the higher or lower giving in a community to the fund raising federation, and to build and compare instruments, based on the configurational, multiple linear regression and other ap proaches, for predicting the performance of a community in term s of the per capita giving.
Scope of the Study
An attempt was made to include in this study all the com munities in the continental United States of America that had a fund-raising federation and population of 25, 000 or more in the year 1950, Those communities, whose population figures as given in the United States census of 1950 differed more than 15 per cent from those given in the 1951 Directory of the Community Chests and 20 Councils of America, Incorporated, were dropped. However, attempts were made to include as many of these as possible after ascertaining their boundaries through directing questionnaires to the respective fund-raising federations. The study included a total of 350 communities, and it was on the basis of the data col
lected for these communities that a number of predictive instru* ments were constructed, A few instruments were also constructed
on the basis of I960 data.
H ypotheses
In order to provide proper focus and direction to the research,
a number of hypotheses were formulated.
Hypothesis I
The study attempted to test the general hypothesis that certain
social and economic characteristics of a community are related
to the higher or lower per capita giving. More specifically, the
following sub-hypotheses were: tested.
Other things being equal, communities having any of the
characteristics listed below are likely to have higher per capita
20 The main reason why such a difference occurs is because the area included in a federation might not be the same as that included in a city, county or other political subdivision by the Bureau of the Census,
16 giving, as compared with those that do not,
1, Larger size of population,
2, Higher median family incomer , .
3, Lesser poverty (lower per cent of families having incomes of less than 2,000 dollars),
4, Higher per cent of families having incomes of more than
$5000,
5, Lesser per cent of nonwhite population,
6, Lower in-migration.
7, Higher per cent of productive population,
8, Higher per cent of craftsmen, foremen, and kindred workers in the employed labor force,
9, Lower per cent of unskilled workers in the employed
labor force,
10, Higher per cent of white collar workers in the employed
labor force,
11, Lower per cent of unemployment,
12, Higher Health Index (indicated by lower infant mortality
ra te ) ,
13, Higher per cent of dwelling units having hot running
water, private toilet and not dilapidated.
Definitions of the factors used in the study appear in Appendix B. 17 14. Higher per cent of dwelling units with mechanical
refrigeration.
15. Higher per cent of persons, 25 years old and over, who
completed high school.
16. Lesser per cent of persons 25 years old and over, who
completed fewer than 5 grades.
17. Higher per cent of persons 14-17 years old enrolled
in schools.
18. Higher median school years completed.
19. Higher industrial index (sum of per capita value added by manufacture, per capita wholesale sales, per capita retail
sales, per capita receipts from personal, business and repair
services and per capita value of farm products sold).
20. Higher ratio of the total amount raised to the goal. 22 21. Higher index on net effective buying income.
22. Higher percentage of population increase.
23. Higher percentage of women employed.
24. Situated in either of these regions: New England,
Middle Atlantic, East North Central or Pacific.
25. Lower crime index (number of burglaries, reported
to the police, per 10, 000 population).
22 Sales Management, Survey of Buying Power, May, 1951.
18 26. Lower juvenile delinquency index (number of auto thefts,
reported to the police, per 1,000 persons 7-17 years old).
27. Higher per capita government expenditure on public
w e lfa re .
28. Higher per capita government expenditure on health and h o s p ita ls .
29. Higher per capita government taxes.
30. Greater participation of people in political affairs (de
termined by ratio of total number of persons who actually voted
to the total population of voting age).
31. Higher ratio of the number of fulltime social welfare
workers to the total population.
32. Higher proportion of non-firm gifts raised by payroll
d ed u ctio n .
33. Higher ratio of volunteers, who worked for the federation,
to the total population.
34. Higher per capita expenditure of the federation for 23 campaign and general administration.
Hypothesis II
Reliable and valid instruments for predicting community
23 Excluding the budget for community welfare council, Social Service Exchange and similar central services.
19 performance in term s of per capita giving to the fund-raising federation can be built on the basis of the Configurational, Multiple
Linear Regression and other approaches.
Hypothesis III
Instruments based on the Multiple Linear Regression predict with greater accuracy and efficiency and are less likely to become outdated with the passage of time as compared with those based on the Configurational, Glueck and Burgess approaches.
Definition of Some of the Basic Concepts
1. Fund Raising Federation. In this study this term is used synonymously for a community chest or a united fund; it does not 24 include ahy other type of such federations.
2. Community. For the purpose of this study, a community denotes any geographical area within which a fundr raising federation launches its campaign.
3. Predictive Configuration, Multiple Linear Regression and Other prediction approaches. These are explained at some length in the next chapter.
Definitions of community chest and united fund have been already given in the foregoing pages.
20 C H A PTER II
REVIEW OF THE LITERATURE
In this chapter some of the important researches that have a bearing on the present study are discussed. These studies broadly fall in three categories--1. those dealing primarily with philanthropic giving and its measurement; 2. those involving the development of tools for measuring the various characteristics or variables of a community, and 3. those which are concerned mainly with the development of such methodology for social pre diction, as is used in the present study.
Studies Concerned with Philanthropic Giving
Russell Sage Foundation Surveys
Since the time of its inception Russell Sage Foundation has included in its studies such subjects as scope and methods of phil- anthropy and the means of its support. A number of publications, based on these studies and concerned directly with philanthropy, have been issued from time to tim e. In 1950 the Foundation pub
lished Andrew's "Philanthropic Giving, " which deals with the major fields of philanthropy.
21 In this book, Andrews presents a very vivid picture of giving in the United States. He found that the lowest income groups (under
$3,000) supply slightly more than 60 per cent of the total philan thropic giving and that these groups contribute at a rate higher than the average for all groups. He points out, however, that a large proportion of the philanthropic giving that comes from the lower income groups goes to the church, and, hence, hospitals, colleges and voluntary welfare agencies are still largely dependent upon the support of higher income groups.
According to Andrews, giving varies not only with income group, but with geography.
. . . the West and Middle West, except Utah., tend toward low rates; the South is intermediate; relatively high rates are confined to the industrial East and a few southern states. . . . A compact block of three states, New York, New Jersey, and Pennsylvania, accounted for nearly a third (32.6 per cent) of the giving in this sample year, although they had less than 26 per cent of the gross individual income.^
Andrews also observes that the increased willingness of em ployers in recent years to make payroll deductions for welfare contributions has facilitated larger gifts from employee groups.
While discussing corporation giving, he remarks that the provision
^Andrews, Philanthropic Giving, op. cit., p. 55.
^Ibid. , pp. 60-62;*
22 of 5 per cent deduction in the Revenue Act of 1955 for charitable contributions had given impetus to corporations to become more substantial donors. He further observes that the community chests or community fund raising federations were usually the largest beneficiaries. In 1950, corporations contributed 40 per 3 cent of the aggregate raised by chests.
In 1952, another study of Andrews entitled Corporation
Giving was published by the Russell Sage Foundation. The book consists of three sections. The first section gives a factual picture of corporate giving and deals with its historical develop ment and its scope and problems. The second section is concerned with the beneficiaries of corporate giving, and the third part deals with legal and tax factors.
He observes that corporations have risen suddenly to great prominence in the field of philanthropy. Basing his information on the reports sent by corporations to the Bureau of Internal
Revenue, he reveals that their gifts and contributions have leaped from a level of 30 million dollars in 1936 to 200 million in 1944.
He further rem arks that since 1944, their contributions had each year exceeded total collections of all fund-raising federations. In
recent years the large majority of corporations are contributing to various philanthropic causes. While giving some of the reasons for increased contributions by the corporations, he says:
Such gifts may increase business through improv ing customer relations. They may result in direct or indirect benefit to employees. They may aid research that will be of later benefit to the corporation. They may help in the education of future employees. By making further governmental expansion unnecessary, they may hold taxation down. By improving com munity facilities, they may help to provide better living conditions for workers and a more prosperous community as custom ers.^
Andrews further observes that although the corporate contri butions constitute only 5 per cent of the total philanthropy, they are a very significant factor in the areas in which corporations usually give. Some of the causes that have "heart appeal" have generated mass collection methods, and so have no need to depend on corporation giving. But there are many voluntary welfare agen cies that are unable to develop mass appeals and have to rely on 5 big gifts, which are usually possible only from corporations.
While comparing the contributions of corporations among themselves, he found that in all asset classes "the rate of giving as compared with profits showed a marked descent as the size of the corporation increased. Thus smaller corporations were
^Andrews, Corporation Giving, op. cit. , p. 18.
^Ibid. , p p. 19-20.
^Ibid. , p. 45. 24 contributing a greater percentage of net profits as compared to the larger ones. He also studied corporation giving in relation to such criteria as number of employees, type of industry and geography. In regard to the question as to where the corporation gifts go, he found that the gifts are distributed as follows: community chests and other welfare agencies, 44.3 per cent; health agencies, 26.6 per cent; education, 21.2 per cent and religious 7 agencies, 4. 1 per cent.
Another study of Andrews, reported in his book Attitudes 8 Toward Giving throws light on some very important facts about giving and fund raising. He says that one of the first principles of fund raising is that to get money one has to ask for it. The manner in which solicitations are made is also very crucial. Thus, friends are usually more effective solicitors; some techniques have better responses than others. Also, a particular technique may be more effective with some than with others.
The study found that social group pressure and community practices exerted a great influence in determining the character and amount of giving. Another strong motivation for giving is
7 Ib id . O F. E. Andrews, Attitudes Toward Giving (New York: Russell Sage Foundation, 1953).
25 self-protection and fear that he himself or a member of his family might some day need help.
Andrews discovered that a considerable amount of giving is prompted by impulse or direct sympathy. Such givers do not care to look to such ultimates as to whether their gift might be contri buted for some better cause. It was also found that people give generously to such causes with which they have personal contact.
He concluded that, by and large, private philanthropy is generally a cc e p te d .
Shapiro's Study of Company Giving
The study was based on a representative sample of all type of companies, having four or more employees, in the Chicago 9 Metropolitan Area. The main objectives of the study were to secure detailed information regarding the actual contribution policies and practices of the companies, and to trace the process by which corporate giving practices are shaped.
Contrary to the findings of Andrew's studies, Shapiro found that larger corporations give more generously than the smaller, not only in terms of total amount but also in relation to their re sources for giving; the same was true for contributions in
9Shapiro, Company Giving. (Chicago, Survey Press, I960).
26 merchandize and loaned personnel and for cooperation in fund raising drives.by encouraging employee participation and permitting payroll deductions.
The study revealed that with the increase in size of corpora tions, the responsibility for administering contributions is more likely to be handled by the central management rather than by the chief executive officer, as is common in the case of smaller corporations. Also as company size increases, the budgeting of contributions becomes more common.
Another fact brought to light by the study was that larger corporations tend to support a greater variety of causes than smaller; and also, whereas the smaller companies contribute more frequently to religious groups and to health causes the larger ones contribute more frequently to such causes as education, youth, recreation and business and civic groups. In term s of per cent of companies making some contributions, the Red Cross was
supported by 70 to 85 per cent of all companies, the fund-raising federation by 67 to 84 per cent and the health organizations by 58 to 70 p e r cen t.
In regard to the trend for the future, the study predicted that the work of contributions would become a regular part of
company activity and that there would be increase in contributions
27 not only in absolute amount but also in term s of the per cent of net profit. The number of different types of causes supported is also expected to increase. Also decisions relating to contributions will increasingly tend to become a m atter of policy than of personal preference; this is particularly true for larger corporations.
The study sounds a rather optimistic note in revealing that there is an increasing search, particularly by larger corporations, for rationality in the field of philanthropy. The study forecasts that with this increase in rationality, there is likelihood for the increase in the scope and range of giving.
In regard to the public attitudes toward corporation contri butions, the study discovered that there was very little awareness of a company's contribution practices and policies even among people who deal with such m atters. It was found that private stock holders as well as 'Financial Analysts' seemed to prefer to invest in a corporation that has a generous and aggressive contribution policy, since they thought that the management of such a corpora tion is more likely to be confident about its future prosperity.
Research by Social Security Administration
The Division of Program Research, Social Security Adminis tration has made a study of voluntary agency expenditures for
28 health and welfare from philanthropic contributions for the period
1930-55.^ The study estimates that during 1955, individuals and corporations in the United States contributed $5,900 million for religious and philanthropic purposes. It reported that living donors gave $5, 100 millions and corporations $400 million and that $400 million came from bequests. Of the total, about $1,925 million was contributed for health and welfare purposes.
It was estimated that in 1955 religious agencies received about $3, 100 million or 53 per cent of all contributions. Approxi mately $1, 150 million or 19 per cent was contributed for-welfare purposes, and $775 million or 13 per cent for health. Philanthropic
contributions for education were estimated as $837 million or about 14 per cent of the total.
The study reported that there were about 5, 000 foundations in the United States, with total assets of about $7.2 billion; that
spent about $400 million in 1954. The study revealed that founda
tions were playing a key role in supporting exploratory research
directed to prevention and cure rather than treatment or relief.
Methods Developed for Determining Fund raising Potential
Thomas Karter, "Voluntary Agency Expenditures for Health and Welfare from Philanthropic Contributions, 1930-55, " Social Security Bulletin, XXI (February, 1958), pp. 14-18.
29 Philadelphia has developed a seemingly practical workable method for measuring fund-raising potential. The Philadelphia plan is based on the assumption that, by and large, contributions to federated campaigns are made from current income; the net disposable income of individuals and the corporate net income after taxes in the fund-raising territory are assumed to be highly re lated respectively to the potential for individual and corporate gifts. The plan further assumes that a good standard can be es tablished for a city on the basis of performance of other comparable
11 c itie s .
The Philadelphia method involves the following steps:
1. A group of cities, that are believed to be comparable to the one for which the fund-raising potential is to be determined, are selected. For each of these cities the total amount raised
during the previous year is split into corporate and non-corporate
g ifts.
2. The "corporate net income after taxes" and the "net
disposable income" for each city are estimated. These estimates
have to be made, since the government data on "corporate net
income after taxes" is available only on a national basis and "net
disposable income" is available only on a state basis.
Method for Measuring Fund Raising Potential (New York: United Community Funds and Councils, 1958). 30 The problem of estimating net disposable income is largely solved by making use of the m easuretof "Net Effective Buying
Income" instead of the former. The figures on the latter measure, which has the same basic definition as the government measure of
Net Disposable Income, are published annually for each county as well as for cities over 25,000 in their publication "Survey of 12 Buying Power" by the editors of Sales Management Magazine.
The "Corporate Net Income after Taxes" is estimated from the "national corporate net income after taxes" and the "number of employees (nationally) by trade group, " which are published annually by the United States Department of Commerce in the
Survey of Current Business-National Income Number. The proce dure for estimating is rather complex and may be stated briefly as follows:
a. the number of employees by trade group in
campaign area is obtained.
b. Ratio of local number of employees to the national
number of employees is computed for each trade group.
c. The national corporate net income after taxes of
each trade group is multiplied by the corresponding ratio obtained
in the preceding step.
*^It may be recalled that Hypothesis I (20) of this study is also based on this measure of "Net Effective Buying Income. " 31 d. The corporate net income after taxes is obtained by summing all the trade group values computed in the preceding ste p .
It may be pointed out that the above mentioned procedure of estimating corporate net income is based on a rather doubtful assumption that corporations of the same trade group and em ploying same number of employees, have approximately the same 13 net income after taxes, irrespective of geographical area.
3. Corporate and non-corporate ratios of contributions to net income for each of these two components are computed for each c ity .
4. "Standard Ratios" for the corporate and non-corporate contributions are established for the community in question on the basis of the ratios derived in the preceding step.
5. Finally, the fund-raising potential for the given commun ity is estimated by adding the products of its corporate and non corporate net incomes and the respective ‘standard ratios,’ established in the preceding step.
13 In an interview with one of the staff members at the Re search Department of the United Community Funds and Councils of America, it was discovered that the estimates of ‘Corporate Net Income’ have not been found valid, and so the method now depends only on the use of the estimates of net Effective Buying In co m e.
32 Methods to Determine Fair Share Quota for Corporations
The appropriate solution to the problem as to what should be the fair share of a corporation to the fund-raising federation or to a national agency drive has encountered many obstacles.
A number of communities, during the recent years, have made attempts to find an answer to the problem.
The Los Angeles Community Chest, preparing for its 1950-
1951 campaign among corporations, first determined the campaign goal for the whole community. Then, on the basis of statistics for fund-raising federations in other cities of comparative size and in consultation with some corporation leaders, it established
35 per cent of the total goal as the corporation quota. The quota for each corporation was, finally, determined on the basis of
ability to contribute (measured by their profits), responsibility to the community (measured by number of employees) and incli
nation to contribute (measured by the amount of contributions in
previous years).i 14
For determining the quota for a corporation by the Los
Angeles plan, it is necessary to have data regarding its profits;
however, many corporations are reluctant to reveal information
regarding profits or sales. Rockford (Illinois) Community Chest
^Andrews, Corporation Giving, op. cit., pp. 78-79. 33 has attempted to sidetrack this problem in a rather interesting manner. In the early 1950's, it used as its yardstick "the average
of one day’s overall payroll, 0. 1 per cent of annual sales, and 1 15 per cent of annual net profit before taxes. The calculations
were to be made by the company itself without revealing any of
the figures.
A number of other communities have also adopted yardsticks
for corporation giving; however, nearly all formulas utilize the
same three factors, with some variations, as used by the Los
Angeles Plan. Some of the other better known plans have been
developed by the fund-raising federations of Philadelphia,
Cleveland, and Syracuse. 16 The corporate yardstick plan developed in Syracuse for its
I960 campaign has some special features and so it may be worth
while to describe it briefly. The Syracuse plan is based on the
concept of "labor content. " It is a concept used in manufacturing V industries to measure the relationship between "payroll" and "value
l5Ibid., p. 161.
l6"The Syracuse Corporate Yardstick" (New York, U. C .F.C ., Bulletin, April, I960).
34 added by manufacture. " It is calculated as follows:
Labor Content Ratio = Payroll ______x iQO. Value Added
Data on both the measures (payroll and value added) needed to compute labor content ratio are available from the publications of'the Bureau of the Censes, United States Department of Commerce.
Some of the advantages claimed by the exponents of the plan are first, the concept is easily understood and used by corporate management; second, labor content is a relatively stable measure and does not fluctuate erratically; third, since labor content cor relates more closely with contributions than any other economic factor tested so far, "it will tend to set standards that conform 18 more to the present pattern of support"; fourth, the data needed to compute labor content are easily available; and fifth, the yardstick is relatively simple to compute. The main disadvantage of the plan is that in its present form it can be used only for manufacturing companies.
17 Ibid. , p. 4. "Value added by manufacture" is calculated by subtracting.the cost of m aterials, supplies, containers, fuel, purchased electric energy and contract work from the value of p ro d u c t. Researches Concerning Development of Method for Studying Characteristics of Communities
Thorndike's Studies
Thorndike made monumental studies which are reported in 19 20 Your City and 144 Smaller Cities, with a view to discover what makes a city a good place to live. He combined 37 items into a single index for scoring each of the 310 larger cities for "good- ness. " 21 Of these 37 items, five measured the healthiness of the city, eight concerned educational opportunities provided by the public, two assessed public recreational opportunities, five dealt with creature comforts such as automobiles, telephones and radios, three measured the degree of literacy and the remaining fourteen were concerned with various other socioeconomic factors.
It may be pointed out, however, that in Thorndike's studies the emphasis is not so much on social or community solidarity or integration as it is on personal opportunities and satisfactions.
Angell has very aptly criticized Thorndike's study by observing that his indexes for personal opportunities and satisfactions are
*^E. Li. Thorndike, Your City (New York: Harcourt, Brace and Company, 1939). 20 E. L. Thorndike, 144 Smaller Cities (New York: Harcourt, Brace and Company, 1940).
21 Later he also made a similar study for 144 smaller c itie s .
36 not enough to measure the goodness of a city. Angell opines that there must be added items "that reflect more adequately moral 22 integration. "
Angell's Studies
Angell made an extensive research with a view to measure what he terms "Moral Integration" of a community. He seems to have borrowed the concept from the great French sociologist,
Emile Durkheim, who in his classic books Suicide and The Ele mentary Forms of Religious Life had demonstrated the importance of m oral integration not only to the individual but also to society.
Angell believed that although small groups were the seedbeds of character, the most challenging problems of moral integration 23 today seemed to lie in very large groups.
Angell defines moral integration in any group as follows:
It is "the degree to which there is a set of common ends and values toward which all the members are oriented and in term s of which 24 the life of the group is organized. " According to Angell, moral integration is the degree to which the areas of possible friction
C. Angell, The Moral Integration of American Cities (Chicago: The University of Chicago Press, 1951), p. 3. or conflict within the group are covered by a set of moral norms that are accepted and implemented by all. In his first study, in
addition to his own data, he made use of some of the data that
Thorndike had gathered concerning 310 cities for his study described
in his book Your City. His conjecture, that a community having a
low moral integration will have high incidence of homicides,
suicides, illegitimate births, deaths from venereal infections,
sales in second hand stores, was found from the results to hold
good.
For his second study, he selected 43 cities of more than
100,000 in the continental United States of America. In selecting
these cities he took into consideration various factors, such as, 25 availability of data or whether the city v/as ‘independent. ‘
He constructed a number of indices for measuring such
variables as welfare effort, crim e, heterogeneity of population and
mobility. Of the nine variables that he tested for their causal re
lation to moral integrity in cities of more than 100, 000, four were
found to have no significance--size, income level, church member
ship and percentage of small businessmen among all businessmen.
25 A city was considered 'independent' if it was not close to any other large city.
38 Five were found to have significant relationship to moral integra- tion--heterogeneity, mobility, rate of city growth, percentage of 26 married women working and rental spread. Of these five, heterogeneity and mobility, which were both negatively correlated with integration, were found to be the key factors, since the in-- fluence of the others was almost completely included in the influence of these two. The multiple correlation of heterogeneity and mobility with moral integration was -.79; this means that 62 per cent of the variation in integration was accounted for by these 27 two factors.
For measuring the moral integration, he constructed a num ber of other indices, such as, welfare effort index, crime index and indices of efforts to support various public services. The wel fare effort index was constructed from the figures contained in a publication of the Children's Bureau of the United States Depart ment of Labor, entitled, The Community Welfare Picture as Re flected in Health and Welfare Statistics in 29 Urban Areas, 1938.
^ It may be noted that five of these nine factors--size, income level, heterogeneity, mobility, and rate of city growth, have been included, with some minor changes, as potential predictors in the present study. 27 Angell, op. cit. , pp. 20-21.
39 In his second study, however, he computed the welfare effort index from data supplied by Community Chests and Councils of America,
Inc, From these data, he constructed the following formula to 28 obtain a welfare effort score:
Amount Raised + Pledgers ______+ A m ount R aise d Quota Number of families ,0033 x Yearly in the area retail sales
His rationale for this formula was that each of the three
ratios measured one aspect of welfare effort--degree of achieve- 29 ment of goal, proportion of families giving, and economic
sacrifice involved. In constructing the formula, Angell seems to
ignore the fact that there might be more than one pledger in a
fa m ily .
His construction of crime index was, in his own words, a
"complicated operation. " He first established standard yearly
frequencies, per 100,000 population for cities between 100,000 and
1,000,000 for three crim es—murder and non-negligent manslaugh
ter, robbery and burglary. Then he divided the standard burglary
frequency by each of the other two and took the square roots of the
28Ibid., p. 124.
^ It will be seen that the ratio of amount raised to quota has been used as a potential predictor in the present study.
40 two quotients. He used these square roots as factors by which to
multiply the number of homicides and robberies for each city before
combining them with the burglaries by addition. Finally, the sums
of the three categories were divided by the 1940 population of each 30 city in thousands to obtain the crime scores.
Another of his measure, termed as heterogeneity index, was
computed by the following formula:
TT . .. Foreign-born whites + 2 (Nonwhites) Heterogeneity = ------r? ~-----— ------— :------—:------.01 (Total Population of City)
He assumed on the basis of prior sociological studies that it is
twice as difficult to integrate a member of another race into a
community predominantly of native born whites as it is to integrate
a foreign born white. But from his work with his own data, he
later came to the conclusion that it was "actually three times as 31 difficult to do so. " This leads one to doubt and wonder whether
such complex mathematical formulas are tenable in social sciences.
Studies of Jonas sen and Peres
Jonas sen and Peres of The Ohio State University have done
extensive and magnificent work in the sphere of deriving a few
30 In the present study two of the factors used were--Ratio of number of burglaries to total population and ratio of number of auto thefts to persons 7-17 years old. It was thought that these were indicators of crime and juvenile delinquency in a community. 31 Angell, op. cit., p. 17. 41 factors by means of factor analysis out of a large number of com- 32 munity variables. They developed measures and computed values uniformly for 82 variables in the 88 counties of Ohio, and, by the use of computers, obtained coefficients of correlation of each of the variable with every other, and thus constructed 82 x
82 inter cor relation table. Then they proceeded to derive out of the
82 variables a small number of independent factors that might explain all the variables.
They used the Thurston centroid method of factor analysis, as modified by Wherry, and extracted from the correlation matrix the following seven factors--Urbanism, welfare, Influx, Poverty,
33 Magnicomplexity, Educational Effort and Proletarianism .
Since, one of the factors which they labeled as welfare is, in many ways, related to the present study, it would be in order to discuss it at some length. According to the authors, a communi ty may be said to be in a state of welfare, if there is absence of
negative conditions--such as, poverty, illness and ignorance,
and presence of positive factors, like health, adequate wealth,
32 The raw data for these variables was collected from the reports of the United States Bureau of the Census and various other agencies.
33 C. T. Jonassen and S. H. Peres, Interrelationships of Dimensions of Community Systems (Columbus, Ohio: Ohio State University Press, I960), pp. 16-24.
42 employment, education and efficiency. . Using AngelPs terminology, such a community would have a high index on "moral integration, " that is, it would be possessing a common core of values so that 34 it is able and willing to act collectively to achieve these values.
The authors have demonstrated that a community possessing high index on welfare has high positive factor loadings on variables
"measuring desirable conditions and high negative loadings on variables describing undesirable factors. " 35 Thus, a community will be likely to have higher loadings on the factor labeled as welfare, if it has high scores on such variables as community
efficiency, welfare self-sufficiency, health index, educational sac
rifice, educational effort, birth rate, population increase and
percentage of craftsmen, foremen and other kindred workers, and
low scores on variables, like, child neglect, unemployment,
crime, mental illness, poverty, extreme incomes arid technical
illiteracy.36
34Angell, op, cit., p. 2.
Jonassen and Peres, op. cit., p. 19. 36 It may be mentioned that they borrowed the m easures for some of the indices, such as: crime and heterogeneity, from the studies of Angell. Researches Concerning Methodology for Social Prediction
The extensive predictions made in the physical sciences have been objects of envy and admiration to social scientists. Some social scientists believe that it is impossible to achieve a high de gree of accuracy in prediction of social phenomena. They argue that the data with which social scientists have to deal with are sub jective, complicated, intangible and non-me as ur able. However,, the pioneering studies of Burgess, the Gluecks, Void, Tibbetts and others have clearly demonstrated that some social phenomena are subject to some degree of observation so as to render predic tion possible.
The earlier social predictive studies were, by and large,
confined to predicting conduct in the field of crim e. The studies
of Burgess, 37 Void 38 and Tibbetts 39 were concerned with the
•^E. W. Burgess, "Factors Determining Success on Parole," Journal of Criminal Law and Criminology, XIX (1928), 239-306. 38 G. B. Void, Prediction Methods and Parole (Hanover, New Hampshire: The Sociological Press, 1931).
•^Clark Tibbetts, "Success or Failure on Parole Can be Predicted: A Study of 3, 000 Youths Paroled from the Illinois State Reformatory, " Journal of Criminal Law and Criminology, XXII (1931).
44 4 0 . prediction of conduct of offenders on parole; the Gluecks, in addition to post-parole conduct of offenders, also, developed ex pectancy tables for the post treatm ent conduct of juvenile 41 delinquents and delinquent women, Monachesi's studies were
concerned with the problem of devising and applying prognostic
techniques to probation treatm ent.
These studies demonstrated that predictions of human be havior were not only possible, but could be of great help to adminis
trators of criminal justice. Later on, attempts were made to apply
the prediction idea to various other social m atters, like marriage,
academic success, and foster home care.
One of the main steps in any predictive study is to determine
the factors or independent variables that are significant to the
outcome or consequence in the phenomenon to be studied. The
more advanced methods of prediction go farther and aim at deter
mining the relative significance of each of the significant factors.
It might be worthwhile to discuss briefly some of the important
40 Sheldon and Eleanor Glueck, 500 Criminal Careers (New York: Alfred A, Knopf, 1930), Chap, XVIII; Five Hundred De linquent Women (New York: Alfred A, Knopf, 1934), Chap, XVII; Unraveling Juvenile Delinquency (New York: The Commonwealth Fund, 1950), Chap, XX,
41 E. D, Monachesi, Prediction Factors in Probation (Hanover, New Hampshire: The Sociological Press, 1932),
45 methods or approaches, developed in the field of social prediction.
Broadly speaking there are three main approaches: Factor Additive,
Multiple Linear Regression and Classification or Configurational.
The Factor Additive approach is mainly of two types-~Burgess's 42 unit-weighting method and the Glueck's percentages weighting
method; 43 it is essentially a means of predicting a qualitative
criterion from a set of quantitative predictors. The method has
been frequently used in predicting parole or m arital success.
Burgess Unit Weighting Method
During the 1920‘s Burgess examined 3,000 cases from three
Illinois correctional institutions. For each specific item, pre
parole life data was related to the parole violation rate. He then
arbitrarily assigned a weight of one point to each item identified
with a violation rate less than the average violation rate for all
offenders. The prediction score of an individual was determined
by adding all the points received. Finally, he developed a predic
tion table based on twenty-one predictive items. According to
this table for offenders receiving a score of 2 to 4 points, the pro
bability was that 76 per cent would fail on parole; for those with
42 Burgess, op. cit. , pp. 239-306.
^Glueck and Glueck, op. cit.
46 a score of 16 to 21 points, only 1.5 per cent were expected to fa il.
In short, this method makes use of the average "success" 44 rate of the total sample as a base. One of the real shortcomings
of this method is that all categories with "success" rates are in
cluded and given the same unit weight, irrespective of the fact
whether a category deviates from the average "success" rate of
the entire sample by a smaller or greater degree. It may be
noted that the method involves a doubtful assumption that the predic
tors are mutually independent and contribute equally to the prediction
of the criterion. Tibbetts and others, however, included only those
"success" categories which deviated from the base rate by a 45 specific amount.
Glueck Method
In 1929> Sheldon and Eleanor Glueck reported on a method
that solved some of the problems raised by the Burgess method.
Instead of comparing the average failure rates with the failure
rate of each item, they used the Pearsonian "coefficient of mean
44 Burgess, op. cit. 45 Tibbetts, op. cit.
47 square contingency” (C) to determine the degree to which each 46 item was related with actual behavior on parole. This proce dure enabled them to select as predictors only those items which had high predictability coefficients. It may be noted that in con trast to Burgess method, relatively few items, usually seven, are used in this method. The percentage of offenders actually violating parole within each subclass of a factor was then deter
mined. The next step was to find the highest and the lowest possible
scores by summing all the sm allest percentages of the subcate
gories of the factors on the one hand, and all the largest percent
ages on the other. Score classes between the two limits were
then established in equidistant intervals. Each case in the sample
was then scored on the selected factors, and placed in the appro
priate score class and appropriate behavior category, the number 47 falling into each score class being converted into percentages.
Thus, on a seven item probability table a man receiving a score
of 274 to 325 would fail only 7. 1 times in 100, while a score of
476 or over indicated a failure rate of 82.8 per cent.
^ S h e l d o n and Eleanor Glueck, Predicting Delinquency and Crime (Cambridge, Massachusetts: Harvard University Press, 1959), p. 25. Method of Multiple Linear Regression
This method is based on fitting algebraic equations describing the relationships between variables by the method of least squares and is used only when the predictors and the criterion to be pre dicted are both in quantitative form. It has been used, often, in predicting academic success or job performance. In using this method it is taken for granted that the distribution of the population is continuous. This assumption, however, may, or may not, hold good in actual studies. In fact, often times, it may not be possible even to know the population distribution. A regression equation is usually derived from a sample drawn at random from the popu lation. Another important assumption involved in this method is that there is a linear type of relationship between the criterion and the predictors.
The relationship between the criterion to be predicted and
the various factors or variables used as predictors is stated in
the following manner:
y=bo+ b ^ t b2x2 + ...... - + b ^
In this equation Xj, X^ ------Xj are the independent variables
or predictors, Y is the dependent variable or the criterion to be
predicted, b^, b2 ------bj are the constants, often called regression
49 coefficients and I is the number of independent variables. The constant b^ is termed the net regression of Y on Xj holding
X_--- X constant; similarly, b is the net regression of Y •5 J C t on X ^ holding constant X^. X ^------X^; and so on for b^ ------b j.
After having estimated the values of all the regression co efficients, some of those independent variables are eliminated whose regression coefficients are found not to be significantly dif ferent from zero, as determined by examining their *t* values.
Finally, those predictors or variables are retained which in com bination with other included variables produce a sufficiently high coefficient of Multiple Correlation with the criterion or dependent variable. An individual's prediction score is obtained by solving the regression equation after simply substituting in it his scores or values on the various predictors. Finally, an experience table is constructed relating the prediction scores and the criterion measures of the individuals in a given sample.
Method of Predictive Configurations
Stuckert has established the effectiveness of the method of predictive configurations, a non-metric method, in constructing 48 instruments for predicting scholastic success. He compared
48 Robert P. Stuckert, "A Configurational Approach to Social Prediction" (unpublished Ph.D. dissertation, Department of Sociology and Anthropology, The Ohio State University, 1956). 50 the predictions resulting from the use of these instruments with those obtained from instruments based on the multiple linear regression, Burgess unit-weighting and Glueck methods. Stuckert found that the instruments based on the method of predictive con figurations were equal or superior to the others. They were not
only easier to use in predicting for subsequent samples, but were
also more accurate and efficient in predicting a criterion with 49 either two or three alternatives. A brief description of the con figurational approach to prediction follows.
The method of predictive configurations, as developed by
Stuckert, is a variation of the prediction by classification approach.
He describes the purpose and the rationale behind the method thus:
It is designed to predict a criterion with discrete categories from a set of discrete or continuous factors on the basis of the principle of maximum probability. Predictive accuracy is assumed to be a function of the homogeneity of the groups being studied as well as the number of variables used in predicting. ^
For constructing a prediction instrument, a sample is divided
into relatively homogeneous sub-samples by taking into account
the factors that are associated with the outcome to be predicted.
49Ibid., pp. 73-74.
^R obert P. Stuckert, "A Configurational Approach to Prediction, " Sociometry, Vol. 21, No. 3 (September, 1958). p. 225. In this way, the probability of each case, included in a sub-sample, having equal chance of achieving a given outcome is increased.
Also, since each sub-sample is defined by a unique configuration of factors, it can be regarded as a separate unit for statistical analysis. Thus, instead of computing the statistical values, needed to predict, for the whole sample, it is sufficient to compute these for each constituent sub-sample. The factors that are used for splitting the sample into sub-samples are selected in such a way that the probability of the predictive outcome occurring for each sub-sample exceeds an arbitrary value, termed as the critical value. Only those configurations of factors are used for prediction that have probabilities exceeding the critical value.
The steps needed for constructing a prediction instrument 51 of this type are listed below.
1. The critical probability values to be used in identifying predictive configurations are established. The major considera tion in this matter is the level of predictive accuracy the researcher aims to achieve. The critical values of .90 and .75 were chosen 52 by Stuckert. This means that any configuration of factors that
^ Ibid., pp. 226-228.
CO For some instruments, he used critical values other than th e s e . was associated with a probability of passing greater than ,90 or a probability of failing greater than .75 was included in the prediction instrument.
2. The initial factor to be used as predictor is selected.
This is done by computing for each factor an array of probability values, representing the criterion distribution. A factor which has one or more categories with probabilities exceeding the critical value or values is selected as the initial predictor and these cate gories form the first item of the instrument. The cases included in these categories are considered predictable and they are elimi nated from the total sample.
3. The remaining categories of the factor selected as the initial predictor are subdivided. The cases falling in these cate gories are classified according to other factors and probability values representing the various criterion categories are computed for these sub-samples. Any of the sub-samples having a probabili ty greater than the corresponding critical values are considered predictable and the cases included in such sub-samples are removed.
This process of adding one factor after another goes on until, either the number of remaining cases becomes so small that it becomes impossible to compute reliable probability values, or the further
division of sub-samples does not produce any significant change
53 in the probability values, irrespective of which factors are added.
These remaining cases are considered non-predictable and placed in a residual classification,
Stuckert is, perhaps, justified in claiming that for social sciences this approach has a number of advantages over the methods 53 based on measurement. First, in the configurational method, the predictors and the criterion may be qualitative, and it is not necessary to assume such characteristics as equality of units or dimensionality for all predictors. Second, unlike the prediction by measurement method, the configurational method does not need to assume that there is a single set of predictors or a single rate of factor weights that is most appropriate for all cases in a given
sample. Third, the measurement methods involve the doubtful assumption that instruments can be designed in such a way that
each m easures only a single dimension with all other conditions held constant. In the configurational approach, there is no need to assume that the effect of any factor on the outcome is the same
for all individuals in the sample. The method thus, provides for the non-additive, non-linear interaction of factors. Fourth, in the
53 Stuckert, "A Configurational Approach to Prediction, " op. c it. , pp. 3 6 -38.
54 prediction by measurement approach it is assumed that every indi vidual in the population is characterized to some degree by every factor. In the configurational method, however, it is not necessary that every individual in the population should possess the character istics of all the factors used in prediction. He needs to possess only the characteristics of the category to which he is assigned.
The configurational method has some disadvantages too.
Firstly, it is necessary to categorize a continuous variable before using it as a criterion. Secondly, the method requires the use of
relatively large sample , because the probability values are deter
mined not on the basis of the total sample, but on the basis of sub
samples, Thirdly, the method enables to predict the outcome only
in the form of a dichotomous or a trichotomous criterion.
55 CHAPTER III
RESEARCH METHODOLOGY
As stated earlier, the purpose of this research was to deter mine various socioeconomic factors that might be related to higher or lower giving to a community fund-raising federation; and to construct and compare instruments, based on the theory of pre dictive configuration, multiple linear regression and other approaches. The fulfillment of this task involved the consideration of a number of problems; some of the important ones were: tenta tive selection of factors to serve as possible predictors, selection of samples, decisions regarding measures, sources of data,
characteristics of the criterion, assumptions, and critical values
needed for constructing configurational instruments. It is impor tant to describe briefly the various procedures.
Selection of Communities
In view of the fact that predictive configuration was one of
the methods to be used, it was essential to have a large number of 1 communities, preferably more than 300. In the beginning stages,
^The necessity for having a relatively large number of cases or communities for the configurational approach is already discussed in the preceding chapter. 56 the w riter was planning to include only those fund-raising federations that covered a population of 50, 000 or more in their campaign for the year 1951. But it was found that the number of such communi ties was only 279, and since some of these could not meet the 2 criteria for being included it was considered necessary to include communities, whose population, according to the United States 3 Census of 1950, exceeded 25, 000. The total number of federations within this category was 465, out of which 115 did not meet the criterion for selection, and so had to be dropped. The study in cluded 350 communities, or more than 75 per cent of all fund-raising federations with a population exceeding 25, 000. Thus, it will be
seen that the selected communities represent a cross-section of
such federations with respect to size of communities, geographical
2 The criteria for selecting a community were: (1) its population figures as given in the census reports and in the Directory of the United Community Funds and Councils for the years 1950 and 1951, respectively did not differ .by more than 15 per cent; (2) its data regarding the amount raised and the goal for the year 1951 were contained in the Directory.
3Those federations were also excluded which, although covered an area with population of 25,000 or more, were located in cities or counties having population of less than 25,000. A federation which served more than one city was included if each city had population of more than 25,000, unless one of these cities, in addition to having a population more than 25, 000, constituted at least 80 per cent of the total population covered by the federa tion. This was necessary, since data for most of the factors used in this study were not available for a community below 25, 000.
57 location and type of organization. Another sample of 100 communi ties or fund-raising federations from the 1961 Directory of the
United Community Funds and Councils of America, Inc., was selected at random out of those communities for which responses to the questionnaire had been received. This sample was used both for the construction of instruments based on I960 data as well as for the validation of instruments constructed on the basis of 1951
Sample (N = 350). A third sample of 50 was likewise selected after excluding the 100 communities, included in the second sample, for validating instruments based on the first two samples. At the time of collecting data for the second sample, the requisite cen sus figures for four states--New York, New Jersey, Illinois, and
California--were not available, therefore no community was in cluded from these states in the second sample; they were included, however, in the third sample.
The total number of fund-raising federations and of those 4 included in the study are given in Table 1. The geographic loca tions of the federations included in the study are presented in
T able 2.
4 The names of the communities, included in the first two samples, are listed in Appendix A. See Table 61 for the names of those included in the third sample.
58 Factors Used as Predictors and the Sources of Data 5 A wide range of factors was used in the present study, in cluding indices on size and geographical location of the community, median family income, heterogeneity, in-migration, unemployment, productive population, white collar workers, dwelling conditions, educational level, business and industry, net effective buying in come, population increase, crim e, juvenile delinquency, employ ment of social workers, payroll deduction ,, volunteers, and budget for fund-raising campaign and administration. The indices were built on the basis of data collected from the various sources,^
In addition, some data were obtained directly from communities by administering a questionnaire to the respective fund-raising federations.
Data on all the thirty-five factors were not available for all the 350 communities. It was only for 113 communities that data
on all the variables could be obtained. For twenty-five factors, however, data were obtained for all the communities and there was no factor on which data for more than half the communities
was missing. On each of the remaining ten factors data on some
5 A list of all the factors used, along with their operational definitions, is given in Appendix B,
^The sources of data are listed in Appendix C.
59 TABLE 1 . TOTAL NUMBER OF FUND-RAISING FEDERATIONS AND OF THOSE INCLUDED IN THE STUDY, BY SIZE OF COMMUNITY 1951 an d 1961
Size of Communities 1951 1961 Those Those In c lu d e d T o ta l included Total Sam ple Sam ple3 I II 25,000 - 49,999 180 134 368 20 . 7 50,000- 99,999 129 98 211 30 18 100,000 - 249,999 100 71 134 30 15 250,000 and over 56 47 85 20 10 T o ta l 465 350 798 100 50 Source: 1951 and 1961 Directory of the United Community Funcfe and Councils of America, Inc. aThese were used only for validation.
TABLE 2 . GEOGRAPHIC LOCATIONS OF FUND-RAISING FEDERATIONS INCLUDED IN THE STUDY, BY REGIONS 1951 AND 1961
Fund-Raising Federations I n c lu d e d R eg io n 1951 1961 1961 Sam ple I Sam ple I I New England 22 10 1 Middle A tlantic 50 8 8 East North Central 89 27 14 West North Central 43 10 7 South Atlantic 45 17 9 East South Central 25 7 1 West South Central 31 9 3 M o untain 8 5 1 P a c i f i c 37 7 6 T o ta l 350 100 50
60 COMMUNITIES INCLUDED IN 1951 SAMPLE . COMMUNITIES INCLUDED IN 1961 SAMPLE I a'II
• € 7 / 7 l •0 v W . /.%,
A ^
v / 5 of the communities were missing for various reasons given below :
1. Data for indices on five factors--crim e, juvenile de linquency, government expenditure on public welfare, health and hospitals* and taxes were available only for cities over 25,000, however, some of the federations were located in counties having no city of this size or if there was a city of this size, it failed to constitute a major part of the community covered by the federation.
The underlying assumption was that if a city constituted a major part of the population of the county in which it was located, the indices for the city on these factors will, as well, hold good for 7 the county,
2. Data on voting were available only for counties, so a federation covering only a city did not have this index unless it formed at least half of the population of the county in which it was located. This was based on a similar assumption as for the five factors mentioned above,
3. Since data on the number of social welfare workers em ployed in a community was available only for cities over 50,000,
7 It might be mentioned that none of these five factors was found to be significant, and so these were not used in the construc tion of the predictive instruments. The test of significance was based on the test of the multiple hypotheses that their regression coefficients were not significantly different from zero,
63 this factor was excluded for communities having a city of smaller s iz e .
4. Data on three factors--payroll deductions, volunteers, and expenditure for campaign and administration were obtained through the questionnaire already mentioned. Responses to the questionnaire were not received from approximately one-third of the federations, and so such communities lacked data on the aforesaid factors.
All the data were coded and punched into I.B.M . cards. All the thirty-five factors were included in the earlier computations of the zero-order correlations and multiple correlations and re gressions. Some of the factors, however, were eliminated at the later stage of the research for various reasons, particularly, lack of significant relationship of a factor with the criterion when hold ing all other factors constant, as determined by its regression co e ffic ie n t.
It may be mentioned, parenthetically, that there might be
many other significant factors that should have been included.
The reason for not including any more factors was mainly due to
the problem of non-availability of the data on such factors uni- \ formly on all or majority of communities. The Criterion
The median per capita giving of all the 350 communities, for the campaign for the year 1951, was used, as the criterion for high and low giving, A community having per capita giving lower or higher than the median was deemed respectively as having lower or higher per capita giving. The median value was $2,06,
Similarly the criterion used for the instruments based on I960 data was $3,82, that is, the median per capita giving for the 1961
sample of 100 communities.
There was some problem in regard to communities falling
near the border line, since a difference of one or two cents in the
per capita giving was enough to move these communities from one
side of the dividing line to the other. To solve this problem, each
of the criterion category was subdivided and four categories were
used; the 40th and 60th percentiles were used as the cutting
points. For the 1951 sample these categories were as follows:
1. Per capita giving of $0. 14 to 1,82
2. Per capita giving of $1.83 to 2.06
3. Per capita giving of $2.07 to 2.29
4. Per capita giving of $2.30 to 5.75
At a later stage there is some further discussion about the
use of these categories in respect to the construction of instruments
65 according to the predictive configurational approach.
Indices of Predictive Accuracy, Efficiency and Stability
In order to measure or evaluate the accuracy, efficiency and stability of each of the instruments constructed, three indices were used, namely, Predictive Accuracy Index, Efficiency Index, and Stability Index. The accuracy of the prediction instruments was drailculated by determining the proportion of cases predicted correctly. In the words of Ohlin and Duncan, the efficiency of 8 prediction is the "percentage reduction in the error of prediction."
Reiss defines it thus: It is
. . . the ratio of the difference between errors in prediction from A and B to error of prediction from A, where B utilizes more or less information than A. It is operationally defined here as the ratio of the difference between error in prediction from the total violation rate and score groups of the prediction instrument to the error of prediction from the total violation rate. ^
®L. E. Ohlin and O. D. Duncan, "The Efficiency of Pre diction in Criminology, " American Journal of Sociology, LIV (1949), 442. Q A. J. Reiss, "The Accuracy, Efficiency and Validity of a Prediction Instrument, " American Journal of Sociology, LVI (1951), 552. For the present study, the word "low giving" may be substituted for "violation. "
66 On the basis of the aforesaid definition, the formula for computing coefficient of efficiency may be written as follows:
Efficiency = ———— N - L
w here
P = number of cases predicted accurately from score groups of the prediction instrument.
L = number of cases in the largest single category of the criterion.
N = total number of cases.
For example, consider a group of 100 communities of which
60 have high giving and 40 low giving. Without any instrument the best prediction for a community drawn at random out of this
group would be high; this procedure would have resulted in 60 cor
rect predictions. Now if the use of a predictive instrument results
in 80 correct predictions, then its efficiency is equal to
80 - 60 _ 2 0 = .5 0 100 - 60 40
The use of the instrument has resulted in 50 per cent in
crease in the number of correct predictions.
The stability of an instrument is "its ability to predict as
efficiently in subsequent samples as in the samples used in its
67 10 construction. " Thus the formula to compute this index can be
11 stated as follows:
Stability = E _ l E 0 w h ere
E^ ~ coefficient of efficiency of subsequent s am p le .
E q = coefficient of efficiency of initial sample,
The Prediction Instruments Constructed on 1950 D ata
A total of sixteen instruments were constructed on the basis
of the data for 1950--two were based on the predictive configuration
method, twelve on multiple linear regression and one each on
Burgess and Glueck methods. A brief description of the instruments,
constructed according to each method, follows.
Predictive Configuration
Two instruments to predict per capita giving were constructed 12 according to the method of predictive configuration. In both, the
^Stuckerfc, op. cit. , p. 46,
11Ibid. 12 The process of constructing an instrument according to this method has already been described in the preceding chapter.
68 criterion was in the form of a dichotomy.
In one of the instruments all 350 communities were used in the construction; the median of the per capita giving of all the com munities was used as the cutting point between the two categories of high and low giving. Such categories are termed contiguous categories. This will be clear from the following graph.
Per Capita Giving
L^ -^-Low — ->j < r ------H ig h------> j Lowest Median Highest
The problem with an instrument constructed on the basis of contiguous categories is that criterion categories might be contam inated by communities falling near the borderline with respect to the per capita giving. The second instrument was made with a view to mitigate this problem; it was decided to exclude the border line communities in building the instrument. Those falling between the 40th and 60th percentiles were categorized as borderline com munities. Thus out of the total of 350 communities, 70 were included in this category; these were not used in the construction of the instrument, though they were used in validating and in comparing with other instruments. This type of categories are termed wide spread categories; these can be represented graphically as follows:
69 Per Capita Giving
j^s- — — Low H igh - - -^ 4
40th 60 th P e r P e r L o w est centile Median centile Highest
As already mentioned in the preceding chapter, one of the steps in the configurational method is to establish the critical probability values. For both configurational instruments, two critical values were used: one representing configurations pre dictive of high giving, and the other representing those of low giving. The values .90 and .75 respectively were selected for the configuration of factors associated with high and low giving.
The factors used for the various configurations included in one or the other instrument are-presented in Figure 1. It will be seen that in each instrument only some of the factors were used; not more than six factors were used in any single configuration.
Median family income was selected as the initial factor since it had two categories with probabilities exceeding the correspond ing critical value. The probability values and the number of
70 communities in each category of this factor are given in Table
3. The probability of low giving of communities included in the first two categories, that is, with median family income below the third decile ($2800) was . 90. So these communities were removed from the total and classified as predictable (see Figure 2).
The next step was to combine the remaining categories to form two groups of communities, for example, in the case of the instrument constructed on the basis of contiguous categories the two groups were: communities with median family income above the fifth decile (median) and those below it. The communities included in each of these groups were classified according to other factors. Those defined by any configuration of factor categories having a probability exceeding the corresponding critical value were considered predictable. It will be seen that the addition of
new factors results in making the subsample more homogeneous than either the total sample or the subsample from which it was
13 The categories were formed by splitting the communities into deciles according to median family income. The categories of all other factors also were formed in a likewise manner in deciles or quintiles. It is interesting to note that the median values of a number of factors such as median family income, and industrial index for the communities included in this study were approximately the same as for the urban and rural non-farm section of the country.
71 Figure 1. Factors Included in the Two Predictive Instruments Based on Predictive Configuration Method.
a I n s tr u m e n t Factor Contiguous W id esp read C a te g o ry C a te g o ry
Median Family Income XX
Dwelling Conditions X
Industrial Index X X
Campaign Budget X X
Campaign Achievement X X
Net Effective Buying Incom e X
Unemployment X X
aThe operational definitions are given in Appendix B.
72 TABLE 3 . NUMBER OF COMMUNITIES AND PROBABILITY VALUES ASSOCIATED WITH CATEGORIES OF MEDIAN FAMILY INCOME, 1951
M edian Family Income Number o f Probability Communities E q u iv a le n t D e c ile Amount H igh Low T o ta l H igh Low
1st Under $2,500 2 33 35 .06 .94
2nd $2,500-2,799 5 30 35 .14 .86
3r d 2,800-3,039 17 18 35 .49 .5 1
4 th 3,040-3,249 15 20 35 .43 • 57
5 th 3,250-3,329 23 12 35 .66 .3 4
6 th 3,330-3,459 21 14 35 .60 .4 0 1 CO — • 7 th 3.460-3,549 24 11 35 .6 9 1
8 th 3,550-3,669 22 13 35 • 63 .3 7
9t h 3,670-3,919 25 10 35 • 72 .2 8
10th 3,920 and over 21 14 35 .60 .4 0 N.B.:-Median Value for the communities (1949) = $3*329. Median Value for Urban and Rural Non-farm Areas of the country (1949) =» $3*324.
73 Figure 2. Probability Values of Predictive Configurations Involving all the 350 Communities: Contiguous categories, Dichotomous Criterion.
H denotes probability of High Giving L denotes probability of Low Giving
(N350)
0L
M edian "Under 3rd Decile" "3rd to 5th "5th Decile F am ily (Under $2,800) Decile" and o v e r" Incom e (N 70)* ($2,800-3,329) ($ 3,330 & o v e r) .10H .90L (N 105) (N 175) . 52H .48L .65H .35L See Figure 3 See Figure 4
* P r e d ic te d Low G iv in g . Figure 3* Probability Values of Predictive Configur ations Involving Communities Having Median Family Income between the Ihird and Fifth Deciles ($2,800-3,329): Contiguous Categories, Dichotomous Criterion, 1950 Data.
(D denotes decile or the tenth part of the distribution in which a community falls) H denotes probability of High Giving L denotes probability of Low Giving
M edian "B etw een 3 rd & 5>th D" Family ($2,800-3,329) Incom e (N 105)
D w ellin g rd D Conditions (Under 6 ii (Over 60$) (N 20) (N 85) .20H . 8o l 58H y v . 42L
I n d u s t r i a l "Under 3rd D" 11 "Over11 3rd D" In d e x (Below $2,300) (Above $2,300) (N l 8 )a (N 67) .22H______.78L .67H .33L
Cam paign Budget (Over 20$) (N 45) (N 22) . 95H .05L . 53H .47L
Cam paign U nder 8th D Achievement ( 101$ & o v e r) (Under 101$) (N 1 7 )b (N 28) .94H .Pep . 29H . 6lL
a P r e d ic te d Low G iv in g . ^Predicted High Giving.
75 Figure 4. Probability Values of Predictive Configur ations Involving Communities Having Median Family Income Above the Fifth Decile ($3,330 and over): Contiguous Cate gories, Dichotoraous Criterion, 1950 Data.
D denotes decile or the tenth part of the distribu tion in which a community falls H denotes probability of High Giving L denotes probability of Low Giving
M edian "6th D & over" F am ily ($ 3*300 & o v e r) Incom e (N 175) • 65H >v^.35L
Cam paign ”4th D & over" "U nder 4 th D" Achievement ( 90$ & o v e r) (Under 90$) (N l 4 l ) (N 3 4 )a • 7^H w^.26L . 24h -76l
Cam paign "7th D & over" " o th e r s " B udget ( 25^ & o v e r) (N 5 l ) b (N 90) .90H______. 10L . 66h _ -34L
I n d u s t r i a l "7th D & over" "U nder 7 th D" In d e x ($ 3,000 & o v e r) (Under $3*000) (N 50) (N 40) .84H .16L • 34h ^ . 66L
Net Effective B uying "8th D & over" "U nder 8th D*f "U nder 4 th D" (*4thD Incom e ($ 1,700 & p v e r) (Under $1700) (Under $1300) & over" (N 21) (N 29) (N 12)a (Over ♦ 90H . 10L • 79H . 21L •08H .92L $1300) (N28) . 57H . 43L
Unemploy " 5 th D & over" "U nder 5 th D" m ent (4 $ 8c o v e r) (U nder 4$) (N 1 0 )D N 19) ♦90H . 10L •74H .26L
aPredicted Low Giving. t>Predicted High B£4ring. 76 Figure 5* Probability Values of Predictive Configurations Involving 280 Communities: Widespread Categories, Dichotomous C riterion.
H denotes probability of High Giving L denotes probability of Low Giving
(N 280)
• 50H .50L
M edian F am ily U nder 3r d "3r d to 6 th "7th Decile and Incom e Decile" D e c ile s o v e r" (Under $2800) ($2800-3,459) (3,460 and over) N 5 4 )* (N 106) (N 120) .00H 1 .0 0 L •53H .47L •70H .3 0 L See figure 7 See figure 6
♦ P r e d ic te d Low G iv in g ,
77 F ig u re 6 . Probability Values of Predictive Configur ations Involving Communities Having Median Family Income Above the Sixth Decile ($3,460-and over): Widespread Categories, Dichotomous Criterion, 1950 Data.
D denotes decile or the tenth part of the distribution in which a community falls H. denotes probability of High Giving L denotes probability of Low Giving
"7th D and over" M edian ($3,460 and over) F am ily (N 120) Incom e
Campaign "5th D & over" " O th e rs " B udget ( 20^ & o v e r) (N 59) (N 61) . 92H .08L .49H . 51L
"U nder 5 th D" "5th D & over" I n d u s t r i a l (Under $2,500) ($ 2,500 & o v e r) In d e x (N 2 3 ) a (N 38) .13H______.871 • 71H .29L
Campaign "U nder 8th D" Achievement (Under 101$) (N 2 k ) .93H______.07L •58H .42L
aPredicted Low Giving bPredicted High Giving.
78 F ig u re J. Probability Values of Predictive Configur ations Involving Communities Having Median Family Income Between the Third and Sixth Deciles ($2,800-3,459): Widespread Categories, Dichotomous Criterion, 1950 Data.
D denotes decile or the tenth part of the distribution in which a community falls H denotes probability of High Giving L denotes probability of Low Giving
M edian "Between 3rd & 6th D" F am ily ($2,800-3,459) Income (N 106) •53H .47L
Campaign "5th D & over" " O th e rs" B udget ( 20^ & o v e r) (N 3 3 )b (N 73) .91H______.09L . 36H .6 4 l
I n d u s t r i a l "U nder 5 th D" "5th D & over" In d e x (Under $2,500) (Under $2,500 & over) (N 3 0 )a (N 43) .07H ______-93L • 53H ______.47L
"U nder 3 rd D" "3 rd D 8c o v e r" Unemployment (U nder 3$) ( 3$ & o v e r) (N 1 6 )a (N 27) ■25H .75L •7 4 H __ * .2 6 l
Campaign " 5 th D 85 o v e r" "U nder 5 th D" Achievement ( 95$ & o v e r) (Under 95$) (N 1 2 )b (N 15) 1.00H .00L •53H .47L
P r e d ic te d Low G iv in g . ’Predicted High Giving,
79 derived. The general rule for determining which factor should be added at any point was to select that which produced the greatest change in the probability values and the smallest decrease in sub sample size.
The process of adding factors was continued until either the number of remaining communities became so small that it was not possible to compute reliable probability values, or the further break-up of a sample failed to result in producing significant changes in the probability values irrespective of which factors were added. The process to develop predictive configurations for the instrument based on contiguous categories, is presented graph ically in Figures 2, 3, and 4. In Figures 5, 6, and 7 the configurations for the instrument having widespread categories are presented.
The predictive configurations associated with high and low categories based upon contiguous categories are shown in Tables
4 and 5 respectively; those for widespread type are presented in
Tables 6 and 7.
Multiple Linear Regression
Twelve instruments were constructed according to the method
of multiple linear regression. In this method the use of a Multiple
Regression Equation is made to predict a dependent variable. The
80 term "multiple" is used to indicate that the equation explains a dependent variable in terms of two or more independent variables.
The constants (b^, b^ . • .) used in the equation are termed Net
Regression Coefficients, and these are determined by an exact mathematical process. The term "net" is used to indicate that the constants show the relation of a dependent variable to two or more independent variables excluding the associated influences 14 of other variables.
The size and number of communities included and the varia bles used for the construction of each of these twelve instruments are presented in Table 8 . It will be in order here to describe the process involved in developing these instruments.
By the use of the Electronic Computer I.B.M . type 709 at the Ohio State University Numerical Computation Laboratory, all possible zero order inter cor relations among the criterion and the independent variables used in the study were computed for the total number of commurities as well as for the four subgroups of 15 communities split according to population size. The data were
■^M. Ezekiel and K. A. Fox, Methods of Correlation and Regression Analysis (New York: John Wiley & Sons, Inc., 1959), pp. 151-169.
^The four subgroups of communities according to popula tion were: 25,000-49,999, 50,000-99,999, 100,000-249, 999, and 250,000 and over.
81 TABLE 4 . PREDICTIVE CONFIGURATIONS ASSOCIATED WITH LOW CATE GORY, SIZE OF SUBSAMPIE AND PROBABILITY VALUES BASED UPON CONTIGUOUS CATEGORIES OF DICHOTOMOUS CRITERION, 1951 (N=350) (D Denotes decile or the tenth part of the distribution in which a given community falls.)
Predictive Subsam ple Balance of the C onf ig ura t iona total communities N Probability N Probability H igh Low H igh Low All communities 350 .5 0 .5 0 l a 70 .10 .9 0 200 .60 .40 lb j 2a 20 .20 .80 260 .63 .3 7 l b j 2b ; 3a 18 .22 • 78 242 .66 .34 l c ; 4 a 34 .24 • 76 208 .7 3 .27 l c ; 4 b , c ; 5a c ; 3a , b ; 6a 12 .08 .92 196 •77 .2 3 apactor categories included in predictive configurations. 1. Median Family Income a. Under $2,800 (under 3rd D) b. $2,800-3,329 (3rd-5th D) $3,330 and over (6th D & over) 2. Dwelling conditions a. Under 60$ (under 3rd D) b . 60# 8c over (3rd D & over) 3. Industrial Index a. Under $2,300 (under 4th D) b. $2,300-2,999 (4th-6th D) c. $3,000 & over (7th D & over) 4. Campaign Achievement a. Under 90$ (under 4th D) b. 90-100$ ( 5t h - 7 t h D) c. 101$ over (8th D & over) 5* Campaign B u d g et a. Under 20^ (under 5th D) b. 20^ & over (5th D & over) c. No record 6. Net Effective Buying Income a. Under $1,300 (under 4th D) b. $1,300-1,699 (4th-7th D) c. $1,700 & over (8th D and over) Includes 121 communities assigned to configurations predictive of High Giving (see Table 5 ) and 75 communities assigned to non-predictable category. The probability of High Giving of the former group of communities was .92 and of the latter group.5 1 .
82 TABLE 5 . PREDICTIVE CONFIGURATIONS ASSOCIATED WITH HIGH CATE GORY, SIZE OF SUBSAMPLE AND PROBABILITY VALUES BASED UPON CONTIGUOUS CATEGORIES OF DICHOTOMOUS CRITERION, 1951 ( N=350)
Predictive Subsam ple Balance of the Configurationa total communities N Probability N Probability H igh Low H igh Low All communities 350 .56 .56 l c ; 2 b ,c ; 3 c 51 .9 0 .10 299 .4 3 .57 lc;2b,c;3a,b,d;4c;5c 21 .9 0 .1 0 278 .4 0 .6 0 lc;2b,c;3a,b,d;4c; 5 a ,b ;6 c 10 • 90 .1 0 268 .38 .62 lb;7b;4b,c,j 3b ,c 22 .95 .05 246 •33 .67 lb;7b;4b,c;3a,d;2c 17 .94 .06 229° .28 .72 aFactor categories included in predictive configurations. 1 . Median Family Income c a. Under $2,800 (under 3rd D f b. $2,800-3,329 (3rd-5th D) c. $3>300 and over ( 6th D & o v e r) Campaign Achievement 6 . Unemployment a. Under 90$ (under 4th D) a. under 3$ (under 3rd D) b. 90-100.9$ (5th-7th D) b. 3-3.9$ (3rd-5th D) c . 1 0 1 $ & o v e r ( 8th D & over) c. 4$ & over (5th D & 3 . Campaign Budget o v e r) a. Under 20^ (under 5th D) 7 . Dwelling conditions b . 2 0 ^ -2 4 9! (5th-6th D) a. Under 60$ (under 3rd c. 25^ & Over (7th D & over) D) d. No record b. 60$ & over (3rd D 4 . Industrial Index & o v e r) a. Under $2,300 (under 4th D) b. $2,300-2,999 (4th-6th D) c. $3,000 & over (7th D 85 o v e r) 5 . Net Effective Buying Income a. Under $1,300 (under 4th D) b. $1,300-1,699 (4th-7th D) c. $1,700 & over ( 8 th D & over)
Includes 154 communities assigned to configurations predictive of Low Giving (see Table 4) and 75 communities assigned to non-predictable category. The probability of Low Giving of the former group of communities was .85 and of the latter group .49. CD denotes decile or the tenth part of the distribution in which a given community falls.
83 TABLE 6 . PREDICTIVE CONFIGURATIONS ASSOCIATED WITH LOW CATE GORY, SIZE OF SUBSAMPLE AND PROBABILITY VALUES BASED UPON WIDESPREAD CATEGORIES OF DICHOTOMOUS CRITERION, 1951 (N=280)
to denotes decile or the tenth part of the distribution in which a given community falls)
P r e d ic ti v e Subsample Balance of the Configurationsa total communities N Probability N Probability H igh Low H igh Low All communities 286 .5 0 ...75G l a 54 .0 0 1 .0 0 226 .62 .38 l b , c ; 2a , c j 3a , b 30 .07 • 93 196 .70 .30 l b , c ; 2a,c; 3c,;4a 16 .2 5 • 75 I8 °b .74 .26 l d ; 2a , c ; 3a ,b 23 .13 .87 157 .83 .17 aFactor categories included in predictive c® hfigutations. 1. Median Family Income. a. Under $2,800 (under 3rd D) b. $2,800-3,329 (3rd-5th D) c. $3,390-3,459 (6th D) d. $3,460 & over (7th D & o v e r) 2. Campaign Budget a. Under 20^ (under 5th D) b. 20^ & over (5th D 85 o v e r) c. No record 3. Industrial Index a. Under $2,300 (under 4th D) b. $2,300-2,499 (4th D) c . $ 2 ,5 0 0 8c over (5th D & o v e r) 4. Unemployment a. Under 3$ (under 3rd D) b. 3$ & over (3rd D & over) ^Includes 118 communities assigned to configurations predictive of High Giving (see Table 7) and 39 communities assigned to non-predictable category. The probability of High Giving of the former group of communities was .92 and of the latter group .56 .
84 TABLE 7- PREDICTIVE CONFIGURATIONS ASSOCIATED WITH HIGH CATE GORY, SIZE OF SUBSAMPLE AND PROBABILITY VALUES BASED UPON WIDESPREAD CATEGORIES OF DICHOTOMOUS CRITERION, 1951 (N=280)
(D denotes decile or the tenth part of the distribution in which a given community falls)
P r e d i c t i v e S ubsam ple Balance of the ~ Configurations 3 total communities N Probability ~~N Probability H igh Low H igh Low
All communities ■ £80”' ' 750 " ' I d ; 2b - 59 • 92 .08 221 .3 9 .61 I d ; 2a , c ; 3c ;4 c 14 • 93 • 07 207 .3 5 .65 l b , c ; 2b 33 .9 1 .09 .25 •75 lb , c ; 2a , c ; 3c ; 5b ; 4 b ,c 12 1.00 .00 162° .1 9 .81 aFactor categories included in predictive configurations. 1 . Median Family Income a. Under $2,800 (under 3rd D) b. $2,800-$3,329 (3rd-5th D) c. i>3,330-$3/159 ( 6t h D) d. $3,460 & over (7th D & o v e r) 2. Campaign Budget a. Under 20^ (under 5th D) b . 20^ 85 over (5th D & over) c. No record Industrial Index a. Under $2,300 (under 4th D) b. $2,300-2,499 (4th D) c. $2,500 & over (5th D & over) 4 . Campaign Achievement a. Under 95$ (under 5th D) b. 95$-100$ (5th-7th D) c . 101$ 8c o v e r ( 8t h D 8c o v e r) Unemployment a. Under 3$ (under 3rd D) b. 3$ & over (3rd D 8c o v e r)
^Includes 123 communities assigned to configurations p r e d i c t i v e o f Low G iv in g ( s e e T a b le 6 ) and 39 communities assigned to non-predictable category. The probability of Low Giving of the former group was .92 and of the latter g ro u p .4 4 .
85 TABLE 8 . NUMBER, POPULATION SIZE OP COMMUNITIES, AND SPECIFI CATION OF THE INDEPENDENT VARIABLES INCLUDED IN THE INSTRUMENTS BASED ON MULTIPLE REGRESSION METHOD, 1951
No. o f v a r i Instrument S iz e o f No. o f a b le s Specification P re s e n te d Communities Commun i n of the vari- i n T a b le i t i e s c lu d e d a b l e s a I 9 25,000 and o v e r 350 24 No. 1-24 II 10 25, 000- 4 9 ,9 9 9 134 24 No. 1 -2 4 III 11 50, 000- 99,999 98 24 No. 1-24 IV 12 100,000-249,999 71 24 No. 1-24 V 13 250,000 and o v e r 47 24 No. 1-24
VI 14 25,000 and o v e r 185 27 No. 1-24 & 32-34 VII 15 25,000-49,999 51 27 No. 1-24 & 32-34 VIII 16 50,000-99,999 56 27 No. 1-24 & 32-34 IX 17 100,000 and o v e r 78 27 No. 1-24 & 32-34
X 18 50,000 and o v e r 102 29 No. 1-24, 30-34 XI 19 50,000-99,999 30 29 No. 1-24, 30-34 7 2 XII 20 100,000 and o v e r 29 No. 1-24, 30-34 a The variables are numbered in the same order as listed in Appendix B.
86 also processed for computing coefficients of regression and of multiple correlations of the independent variables with the cri terion. The "t" and "F" values were also computed for each regression coefficient and multiple correlation respectively to test their significance. ^ Later on Multiple Hypotheses were also tested to determine which set of variables might be eliminated without causing significant reduction in the value of Multiple Cor relation. computed when all the variables had been included. For specifying the variables to be included in each of the multiple hypotheses the process described below was adopted.
The "t" values of regression coefficients for the variables were arranged in a sequence from the lowest to the highest. For the first multiple hypothesis five variables having the lowest "t"
^M , Ezekiel and K. A. Fox, op. cit. t denotes the ratio of a regression coefficient to its standard error. Thus t, = D c. * where b is regression coefficient and S, is the standard °b error of the regression coefficient. The "t" is used to estimate the probability that an observed value of b might have been obtained by chance in random sampling from a population in which the true regression coefficient was zero. F is the ratio for testing the significance of the coefficient of Multiple Determination (R2). R2 (N-V-1) F = T(LRZj where N is the number of communities and V is the number of independent variables.
87 values were included; each of the succeeding hypothesis included one after another of the remaining variables in the order from low to high with respect to their "t" values. This process of adding the variables was continued until only two having the highest "t"
values remained.
By the use of the Multiple Hypotheses it was possible to
decide which set of variables should be included in a predictive
regression equation; the aim was to include variables whose
regression coefficients, as determined from their "t" values, 17 were significantly different from zero.
The values of coefficients of regression and multiple cor
relation and the corresponding "t" and "F" values for each of the
equation are given in Tables 9 to 20.
After having constructed the twelve instruments according
to the method of multiple linear regression, the next problem was
to select four of these for the purpose of validation, one for
communities 25,000 and over, and one from each size category:
25,000-49, 999, 50,000-99, 999, and 100,000 or over. In making
this selection the main considerations were: the values of the co- 2 efficient of Determination (R ) and the "F" ratio for determining
its significance should be as high as possible and the level of
17 The 5 per cent level of significance was used.
88 significance of the Regression Coefficients of each of the variables should be high. The four instruments finally selected were those presented in Tables 14, 15, 16, and 17 respectively.
As each factor was successively added the coefficients of 2 Multiple Correlation (R) and Multiple Determination (R ) obtained for each of the three selected instruments according to size categories are presented in Tables 21, 22 and 23. It will be seen from these that the inclusion of further variables after first six or seven produced very small increase in the coefficients of Multiple Determination. It is worth-while to note that the coeffi cients of Multiple Determination reveal what proportion of the variance in the dependent variable is associated with variances in the independent variables included. From the aforesaid tables it will be seen that more than 80 per cent of the variance in giving was associated with the variables used in the study. In other
words only less than 20 per cent of the variance remained un
explained.
89 TABLE 9 . COEFFICIENTS OF REGRESSION ( b ) , MULTIPLE CORRELA TION (R ), MULTIPLE DETERMINATION (R2 ) , " t " AND "F" FOR VARIABLES INCLUDED IN MULTIPLE LINEAR REGRESSION EQUATION FOR COMMUNITY SIZE 2 5 ,0 0 0 AND OVER, 24 VARIABLES, 1951 (N=350) !
L e v el o f Variables Included "b" "t" Significance Campaign Achievement (X 3 ) 3*089 7«54 .001 Industrial Index ( X 2 J 0 .0 1 9 5-10 .001 Unskilled Workers (X 3 ) A.482 4.75 *001 In-migration (X4) -6.601 -4.65 .001 White Collar Workers (X5 ) 3*602 3*78 Q001 Dwelling Conditions jxgj 1.350 3.64 .001 Not Effective Buying Income (X 7 ) 0.062 3*11 .001
R = .7 0 2 R 2 = .493 F = 47.35 (.001)
bQ = -6 0 4 .1 0
The Regression Equation was
Y = -604.10 + 3.089Xx + 0.019X2 + 4.482X3 - 6.601X4
+ 3.602X5 + I. 35OX5 + 0 . 062X7 w here
Y = Prediction per capita giving
X^------X7 = Independent variables indicated above.
90 TABLE 10. COEFFICIENTS OF REGRESSION ( b ) , MULTIPLE CORRELA TION (R ), MULTIPLE DETERMINATION R2 ) , " t w AND "F" FOR VARIABLES INCLUDED IN MULTIPLE LINEAR REGRESSION EQUA TION FOR COMMUNITY SIZE 2 5 ,0 0 0 -4 9 ,9 9 9 , 24 VARIABLES, 1951 (N = 1 W
L e v e l o f Variables Included "b" ”t" Significance Unskilled Workers (Xi) 5-501 4.30 .001 White Collar Workers (Xo) 4.140 3-12 .001 Net Effective Buying Income 1X3 ) 0.095 2.86 .001 Industrial Index (x4) 0.020 2.72 .01 Campaign Achievement 1X5 } 1.762 2 .3 4 .0 1 In-migration (X 6 ) - 5 .290 -2.15 *05 Dwelling Modernity (X 7 ) X.820 2.03 .05
R = .7 0 3 R 2 = .494 F = 17.61 (.001)
b0 = -659.85 The Regression Equation was
Y = -6 5 9 .8 5 + 5 - 501X1 + 4.140X2 + O.O95X3 + 0 . 020X2!
+ 1.762X5 •- 5.29OX5 + 1.820X7
w here
Y - Predicted per capita giving
X j------Xj = Independent variables indicated above.
91 TABLE 11. COEFFICIENTS OF REGRESSION (b )* MULTIPLE CORRELA TION (R ), MULTIPLE DETERMINATION (Rd ) , " t " And 1F 1FOR VARIABLES INCLUDED IN MULTIPLE LINEAR REGRESSION EQUA TION FOR COMMUNITY SIZE 5 0 ,0 0 0 -9 9 ,9 9 9 , 24 VARIABLES, 1951 (N=9B1
L e v e l o f Variables Included "b" " t " Significance
Unskilled Workers (X i 1 9 .5 1 0 5 .1 6 .001 Campaign Achievement x2 l 3 .2 5 8 4 .9 3 .0 0 1 Dwelling Conditions X3 1 2 .3 4 0 3 .8 5 .0 0 1 White Collar Workers (X4 1 4 .7 8 0 3 -0 4 .0 1 Unemployment (x5iI-II .830 - 2 .5 5 .0 2
R = .661 R2 = .437 p « 14.14 (.001)
bQ = - 783.92
The Regression Equation was
Y r - 783.92 + 9.5lOXi + 3-258x2 + 2. 340X2 + 4.78ox2+
- 11.830X 5
Where
Y = Predicted per capita giving
X i X5 = Independent variables indicated above.
92 TABLE 1 2 . COEFFICIENTS OF REGRESSION (b )* MULTIPLE CORRELA TION (R ), MULTIPLE DETERMINATION (R‘ ) , " t " AND "F" FOR VARIABLES INCLUDED IN MULTIPLE LINEAR REGRESSION EQUA TION FOR COMMUNITY SIZE 1 0 0 ,0 0 0 -2 4 9 ,9 9 9 , 24 VARIABLES, 1951 ( N = m
L e v e l o f Variables Included "b" " t " Significance
In-migration (X!)-l4.760 -5.62 .001
Net Effective Buying Income (x 2 ) 0.199 5 .1 0 .001 Campaign Achievement (X3 ) 4 .0 0 0 4 .6 1 .001
Dwelling Modernity (X4 ) ^.0.650 -O .58 .3 0
R = .7 5 9 R2 = -575 F = 2 2 .3 7 ( . 001)
b0 = -3 0 8 .9 9 The Regression Equation was
Y = -308.99 - 14.760X]. + O.I 99X2 + 4.000X 3 O .65OX4
w here
Y = Predicted per capita giving
X]_------X4 = Independent variables Indicated above.
93 TABLE 1 3 . COEFFICIENTS OF REGRESSION ( b ) . MULTIPLE CORRELA TION (R ), MULTIPLE DETERMINATION (R 2 ), " t " AND "F" FOR VARIABLES INCLUDED IN MULTIPLE LINEAR REGRESSION EQUA TION FOR COMMUNITY SIZE 2 5 0 ,0 0 0 AND OVER, 24 VARIABLES, 1951 (N=4T) 1
L e v e l o f Variables Included "b" «.t » Significance High School Enrollment ( x l ) 12.140 4.04 .001 Campaign Achievement U 2 ) 4.206 2.93 .01 High School or more E d u c a tio n U 3) - I I .77O -2.84 .01 Unemployment x4 -24.071 -2.61 .01 C ra ftsm e n (X5 ) -17.950 -2.55 .01 Population Size (in th o u s a n d s ) (*6) 0.049 2.46 .01 Median School Years C om pleted 70.670 2.26 .10 Dwelling Conditions a 1.780 1.53 .10 R = .791 R2 = .626 F == 7.94 (.001)
bQ = -1 2 1 .1 6
The Regression Equation was
Y = -121.16 + 12.140X-L + 4 . 206X2 - 11.770 X3 - 2 4 .O7 IX4
- 17.950X5 + 0 . 049X6 + 7 0 .6 7 0 Xj + 1,780x3
w here
Y = Predicted per capita giving
Xi X8 = Independent variables indicated above.
94 TABLE 14. COEFFICIENTS OF REGRESSION ( b ) . MULTIPLE CORRELA TION (R ), MULTIPLE DETERMINATION (R2 ) , " t" AND "F" FOR VARIABLES INCLUDED IN MULTIPLE LINEAR REGRESSION EQUA TION FOR COMMUNITY SIZE 2 5 ,0 0 0 and OVER, 27 VARIABLES, 1951 (N=l851
L e v e l o f Variables Included "b" »t rr Significance Campaign Budget (X l) 4.0542 8.69 .001 Campaign Achievement (x2 ) 2.5124 4.01 .001 Net Effective Buying Incom e X3 0.0808 2.96 .01 Payroll Deductions x4 0.8045 2.79 .01 Campaign Volunteers x j 0.1244 2.68 .01 L e ss th a n 5 g ra d e s E d u c a tio n (X6) - 3.0764 - 2.53 .01 Industrial Index (Xy) 0.0104 2.34 .02 High School or more E d u c a tio n (X8) -1.6384 - 2.28 .02
R = .755 R2 = .5 7 0 F = 29.19 ( . 001)
b0 = -183.76
The Regression Equation was
Y = -183.76 + 4.0542X1 + 2.5124X2 + 0.0808X3 + 0.8045X4
+ 0 .1 2 4 4 x 5 = 3 . 0764x5 + 0.0104x7 - 1.6384Xq
w here
Y = Predicted per capita giving
Xj Xg = Independent variables indicated above.
95 TABLE 1 5 . COEFFICIENTS OF REGRESSION ( b ) . MULTIPLE CORRELA TION (R ), MULTIPLE DETERMINATION (R2 ) , " t " AND "F" FOR VARIABLES INCLUDED IN MULTIPLE LINEAR REGRESSION EQUA TION FOR COMMUNITY SIZE 2 5 ,0 0 0 -4 9 3 999, 27 VARIABLES, 1951 (N=51)
L e v e l o f Variables Included "b" 111" Significance
Campaign Budget X 1 3 .9 2 2 1 6.28 .001 Women Em ployed x2 6.7672 2 .7 4 .01 Population Increase -0 .9 8 2 4 .02 X3 2 .3 5 High School Enrollment x 4 4 .8 2 6 9 1 .9 9 • 05 Geographical Location x 5 0 .0 4 9 7 1.78 .05 Campaign Volunteers \X6) 0 .1 4 4 4 I .27 .02 Poverty Index ( x7 ) 1.4787 0 .9 4 .2 0
R = .818 R2 = .668 F = 12.38 (.,001)
bQ = r. 69 7 . 07
The Regression Equation was
Y = -697.07 + 3• 9221XX + 6 . 7672X3 - O .9824X3 + 4 . 8269X4
+ 0 . 0497X5 + 0 . l 444x 6 + 1 . 4787X7.
liie r e
Y = Predicted per capita giving
X-^— ----- Xy = Independent Variables indicated above.
96 TABLE 1 6 . COEFFICIENTS OF REGRESSION ( b ) . MULTIPLE CORRELA TION (R ), MULTIPIE DETERMINATION (R2 ) , " t" AND V FOR VARIABLES INCLUDED IN MULTIPLE LINEAR REGRESSION EQUA TION FOR COMMUNITY SIZE 5 0 ,0 0 0 -9 9 ,9 9 9 , 27 VARIABLES, 1951 (N=5F)
L e v e l o f Variables Included "b" " t " Significance
Campaign Budget (*!) 5.8234 4.56 .001 Poverty Index ( x 2 ) - 7.5490 - 3.05 .01 Population Size (in T h o u sa n d s) 0 0 1.2803 1.74 .05 Unemployment Index (X 4 ) 12.5860 1.67 .05 Health Index x5 2.6825 1.61 .10 Geographical Location X6 - 0.0493 - 1.53 .10 Dwellings Modernity ( X 7 ) - 3.7252 - 1.35 .1 0 R = .702 R2 = .4 9 3 P = 6.67 ( . 001)
b 0 = 535 -4 6 0
The Regression Equation was
Y = 535-46 + 5-8234X1 - 7.5490X2 + 1.2803X3 + 12. 5860X^ + 2.6825X5 - 0.0493X6 - 3*7252X 7
w here
Y = Predicted per capita giving
X-^------Xy = Independent variables indicated above.
97 TABLE 1 7 . COEFFICIENTS OF REGRESSION ( b ) . MULTIPLE CORRELA TION (R ), MULTIPLE DETERMINATION (R2 ) , " t " AND "F" FOR VARIABLES INCLUDED IN MULTIPLE LINEAR REGRESSION EQUA TION FOR COMMUNITY SIZE 1 0 0 ,0 0 0 KND OVER, 27 VARIABBLES 1951, (n=7H1
L e v e l o f Vairables Included "b" " t" Significance 5-4415 .001 Campaign Budget X1 6.26 In-m igration U p -1 3 .3 3 5 0 - 4 .8 5 .001 Campaign Achievement x 3 3 .8 2 2 0 4 .6 2 .001 Campaign Volunteers U/,5 0 .3 5 5 0 4 .2 7 .001 Median School Years C om pleted (x5) 22.7200 3 -3 8 .0 1
R = .857 R2 = .735 F = 3 9 .8 8 (.0 0 1 ) b0 = -436.76
The Regression Equation was
Y = - 436.76 + 5 .4 4 l5 X 1 - 13.•3350X2 + 3 . 8220X3 + 0 . 3550X4 + 22.7200X 5
w here
Y = Predicted per capita giving in cents
X I X5 = Independent variables indicated above.
98 TABLE 1 8 . COEFFICIENTS OF REGRESSION ( b ) . MULTIPLE CORRELA TION (R ), MULTIPLE DETERMINATION (R2 ) , " t " AND "F" FOR VARIABLES INCLUDED IN MULTIPLE LINEAR REGRESSION EQUA- TION FOR COMMUNITY SIZE 50,000 and OVER, 29 VARIABLES, 1951 (N=102)
L e v e l o f Variables Included " b ” " t" Significance Campaign Budget (X ii) 5 .5 H 5 .9 8 .001 Campaign Achievement (x2 ,) 3 -3 9 5 4 .1 7 .001 Net Effective Buying Incom e (X3 I) 0 .1 3 5 4 .0 4 .001 In-m igration (x 4, -9 -3 2 0 - 3.68 .001 Payroll Deductions 1 .2 4 1 3 .4 8 .001 Social Welfare Workers X5 7 .3 5 0 2 .9 3 .01 Health Index (x7 ) 4 .1 0 1 2.87 .01
R = .794 R2 = .631 F = 22.98 ( .0 0 1 )
b 0 = -5 3 8 .4 3 The Regression Equation was
Y = -538.43 + 5.5HX •395X2 + 0. 135X3 ■-• 9..320XJ, 1 + 3 + 1 . 241x 5 + 7 .350x5 + 4 . i o i x 7 where Y = Predicted per capita giving
Xi X7 = Independent variables indicated above.
99 TABLE 1 9 . COEFFICIENTS OF REGRESSION ( b ) . MULTIPLE CORRELA TION (R ), MULTIPLE DETERMINATION (R5 ) , " t " AND "F" FOR VARIABLES INCLUDED IN MULTIPLE LINEAR REGRESSION EQUA TION FOR COMMUNITY SIZE 5 0 ,0 0 0 -9 9 ,9 9 9 , 29 VARIABLES, 1951 (N=3?) ~
Level of Variables Included "b" "t" Significance
Campaign Budget (X i) 7 -7 2 4 3 .4 5 .01
Health Index (x2 ) 5 .4 7 0 2 .4 9 .01
Campaign Achievement (x3) 3 .9 9 8 2 .3 9 .02
Social Welfare Workers (X4 ) 9 .5 7 0 1 .3 5 .1 0
R = .677 R2 = • ^59 F = 5 -3 0 ( .0 0 1 ) bQ = -519.07 The Regression Equation was
Y =-519.07 + 7-724Xx + 5.470X 2 + 3.998X3 + 9 . 57OX4
w here
Y = Predicted Per capita giving
X i------X4 = Independent variables indicated above.
1 00 TABLE 2 0 . COEFFICIENTS OF REGRESSION ( b ) . MULTIPLE CORRELA TION (R ), MULTIPLE DETERMINATION (R2 ) , " t " AND "F" FOR VARIABLES INCLUDED IN MULTIPLE LINEAR REGRESSION EQUA- TION FOR COMMUNITY SIZE 1 0 0 ,0 0 0 AND OVER, 29 VARIABLES, 1951 (N=72)
L e v e l o f Variables Included "b" »»t « Significance
Campaign Budget (x -,;1 5 .3 1 1 4 .8 6 .001 Campaign Achievement X2 1 4 .2 2 3 4 .1 2 .001 Payroll Deductions X3 1 1 .3 4 0 3 .2 5 .01 Social Welfare Workers m ) 1 6 .3 4 0 2 .2 4 .02 Net Effective Buying Incom e (x^;) 0 .0 7 4 1 .9 3 .05 Health Index (Xgl1 -3 -1 7 0 - 1 .6 9 .05
R = .818 R 2 = .669 F = 2 1 .8 9 (. 001) bQ = -374.46
1516 Regression Equation was
Y = -374.46 + 5-3HX-L + 4 .223X3 + l.34ox3 + 6 . 340X4
+ 0 .0 7 4 x 5 - 3.170X 6 ?
w here
Y = Predicted per capita giving
X i------X5 = Independent variables indicated above.
101 TABLE 2 1 . VARIABLES PROGRESSIVELY INCLUDED AND THE RESULTING COEFFICIENTS OF MULTIPIE CORRELATION (R ), MULTIPLE DETERMINATION (R2 ) AND "F" RATIOS FOR THE INSTRUMENT SELECTED FOR COMMUNITY SIZE 2 5 ,0 0 0 -4 9 ,9 9 9 , 27 VARIABLES, 1951 (N=51)
L e v e l o f Variables Progressively S i g n i f i Included and R R2 »F» c an ce Used in the Instrument: Campaign Budget .681 .464 High School Enrollment .724 • 524 26.38 .001 Population Increase • 751 • 564 2 0 .2 9 .001 Campaign Volunteers • 772 • 596 16.96 .001 Women Em ployed .802 .643 1 6 .2 0 .001 Poverty Index .803 .644 1 3 .2 6 .001 Geographical Location .818 .6 6 8 1 2 .3 8 .001
Not Used in the Instrument:
High School or more Education .819 .671 10.70 .0 0 1 Campaign Achievement .829 .688 1 0 .0 5 .001 Less than 5 grades education .840 .706 9 .6 1 .001 Payroll Deductions .857 • 734 9 .7 9 .001 C ra ftsm e n .870 .756 9 .8 3 .001 Dwelling Modernity • 875 .767 9-38 .001 Median School Years Completed .878 • 772 8 .7 1 .001 In-m igration .882 • 778 8 .1 6 .001 Unemployment .882 .778 7 .4 6 .001 Dwelling Conditions .883 .780 6.90 .001 Unskilled Workers . 885 .783 6 .4 3 .001 . Bopulation Size .885 .783 5 .9 0 .001 Well-to-do Families .894 .800 5-99 .001 Non-white Population .895 .801 5-55 .001 Industrial Index Net Effective Buying Income Median Family Income Health Index White Collar Workers Productive Population .896 .802 3 -4 6 .0 1
102 TABLE 2 2 . VARIABLES PROGRESSIVELY INCLUDED AND THE RESULTING COEFFICIENTS OF MULTIPLE CORRELATION: (R ), MULTIPLE DETERMINATION (R2 ) AND "F" RATIOS FOR THE INSTRUMENT SELECTED FOR COMMUNITY SIZE 5 0 ,0 0 0 - 9 9 ,99B 27 VARIABLES, 1951 (N=56) 9
L e v e l o f Variables Progressively- S i g n i f i Included and R R2 Hpl. can ce Used in the Instrument: Campaign Budget •576 •332 Health Index •591 .349 1 4 .1 9 .0 0 1 Poverty Index •655 .429 13.01 .0 0 1 Geographical Location .659 • 435 9 -8 1 .001 Dwellings Modernity .663 .440 7.85 .001 Population Size .681 .464 7 .0 6 .001 Unemployment .702 .493 6.67 Not Used in the Instrument: Non-white Population .702 .493 5-72 .001 Industrial Index .706 .499 5 .0 9 .001 Median School Years Completed • 724 .524 4.96 .001 Campaign Achievement .747 .542 4 .7 4 .001 Payroll Deductions •754 .569 4 .7 3 .0 0 1 Dwelling Conditions •754 .5 6 9 4 .2 7 .0 0 1 Population Increase .766 • 587 4 .1 6 .0 0 1 Net Effective Buying Income •769 • 591 3 .8 5 .001 C ra ftsm e n •772 .596 3 .6 0 .0 0 1 Productive Population .784 .615 3 .5 7 .001 Less than 5 Grades Education • 794 .630 3 .5 0 .001 W ell-to-do Families .811 .658 3 .6 4 .001 High School or more Education .812 .660 3 .4 0 .01 Campaign Volunteers .813 .661 3 .1 6 .01 Median Family Income In-m igration Unskilled Workers White Collar Workers
High School Enrollment 00 1 —( Women Em ployed • .663 2 .0 4 .05
103 TABLE 2 3 . VARIABLES PROGRESSIVELY INCLUDED AND THE RESULTING COEFFICIENTS OF MULTIPLE CORRELATION (R ), MULTIPLE DETERMINATION (R2 ) AND "F" RATIOS FOR INSTRUMENT SELECTED FOR COMMUNITY SIZE 100, 000 and . OVER, 27 VARIABLES (N=78)
L e v e l o f Variables Progressively- S i g n i f i - Included and R R2 F can ce Used In the Instrument: Campaign Budget In-m igration .687 .472 3 3 .5 6 .001 Campaign Achievement .767 .588 3 5 .2 8 .001 Campaign Volunteers .832 .6 9 3 4 1 .1 2 .001 Median School Years Completed • 857 • 735 39-88 .001 Not Used in the Instrument:
White Collar Workers .862 • 744 3 4 .3 4 .001 Unskilled Workers .862 .744 2 9 .0 3 .001 Dwelling Modernity .870 • 756 2 4 .0 6 .001 Poverty Index .884 .782 23-42 .001 Geographical Location .885 .784 2 1 .7 3 .001 Women Em ployed .885 .784 2 0 .3 8 .001 Less than 5 Grades Education .885 .784 19 .6 3 .001 Median Family Income .885 .784 1 7 .8 5 .001 Payroll Deductions .886 • 785 1 6 .4 1 .001 Non-white Population .888 .788 1 5 .3 6 .001 Net Effective Buying Income .891 • 794 1 4 .6 9 .001 We11-to-dO Families .891 .794 1 3 .6 0 .001 Health Index .891 .794 12.65 .001 Dwelling Conditions .892 • 795 11.83 .001 High School or more Education .892 .795 11.05 .001 Population Size .894 .800 10 .6 4 .001 Productive Population C ra ftsm en High School Enrollment Population Increase Indusfefcial Index .907 .823 8.63 .001
104 Burgess Unit Weighting Method
An instrument to predict a dichotomous outcome was developed according to the Burgess method. 18 In constructing the instrument only those factor categories that had rates significantly different from the base or average rates were retained; each such category was assigned a unit weight. To determine whether a category was significantly different from the average rate of the total sample, the use of the critical ratio was made. Factors included in the in
strument and their probability values are given in Table 24. These factors were then used in constructing an experience table, show ing the relationship between prediction scores and criterion cate
gories (see Table 25). Lastly the distribution of prediction scores was dichotomized at the point where maximum predictive accuracy
could be obtained (see Table 26).
Glueck Method
An instrument for predicting high or low giving was constructed
according to this method. The coefficients of contingency were
computed for all the variables that were significantly correlated 19 with giving. Six variables having the highest coefficient of
18 The process of developing an instrument according to this method is already discussed in the preceding chapter.
197The use of correlation m atrices was made in this p ro c e s s . TABLE 2 4 . CRITICAL CATEGORIES AND PROBABILITY VALUES OP FACTORS INCLUDED IN PREDICTIVE INSTRUMENT BASED ON BURGESS METHOD, 1951
(D denotes decile or the tenth part of the distribution in which a given community falls)
P ro b a b i l i t y o f H igh F a c to r C ritical Category G iv in g Campaign Budget 20^ 8s over (5th D 8s over) .80 Payroll Deductions 20$ 8s over (8th D 8s over) •77 Industrial Index $2,500 8s over (5th D 8s o v e r) •72 Well-to-do Families 20$ & over (5th D 8s over) •71 Campaign Volunteers 1$ 8s over (8th D 8s over) • 70 Poverty Index Under 20$ (lst-4th D) •67 Dwelling Conditions 70$ 8s over (5th D 8s over) .6 6 Net Effective Buying Incom e $1500 8s over (6th D 8s over) .66 In-m igration Under 5$ (lst-4th D) .65 Median Family Income $3,250 8s over (5th D 8s over) .65 Campaign Achievement 95$ & over (5th D 8s over) .6 5 High School or more E d u c a tio n 25$-52$ (3rd D-8th D) .64 Dwelling Modernity 87*5$ & over (5th D 8s over ) .63 Women Em ployed 30-34.9$ (5th-9th D) • 63 Less than 5 Grades E d u c a tio n 4.8-8.5$ (3rd-6th D) .6 2 Population Increase Under 20$ (lst-6th D) .62 Geographical Location N. England, Mid A tlantic, E.N.Central, W.N.Central Mountain, Pacific .61 High School Enrollment 85$ & over (4th D 8s over) .60 Median School Years - Completed 10.3 & over (5th D 8s over) • 59 Voting Behavior 60$ 8s over (5th D 8s over) • 59 White Collar Workers 40$ 8s over (5th D 8s over) • 58
106 TABLE 2 5 . PREDICTION SCORES PROM BURGESS INSTRUMENT AND PER CAPITA GIVING FOR 185 COMMUNITIES, 2 5 ,0 0 0 AND OVER, DICHOTOMOUS CRITERION, 1951
P e r capita Giving P r e d ic tio n Borderline Communities S c o re s E x c lu d e d 3 A ll Communities N H igh Low N H igh Low 0 - 2 7 0 7 10 0 10 3 - 4 15 3 10 20 6 14 5 - 6 12 5 7 15 5 10 7 - 8 15 3 12 19 7 12 9 -1 0 11 7 4 22 12 10 11-12 15 12 3 24 17 7 13-14 37 32 5 45 36 9 1 5 -1 6 14 10 4 18 13 5 1 7-21 12 12 — 12 12 — T o ta l 138 §6 32 185 108 77
TABLE 26. DICHOTOMY OP PREDICTION SCORES PROM BURGESS INSTRUMENT AND PER CAPITA GIVING: 185 COMMUNITIES, 2 5 ,0 0 0 AND OVER, DICHOTOMOUS CRITERION, 1951
Per capita Giving P r e d ic tio n Borderline Communities S c o re s Excluded3 All Communities N H igh LOw N H igh Low CO 0 1 49 13 36 64 18 46
9 -2 1 89 73 16 121 90 31 T o ta l 138 86 52 185 108 77
aBorderline communities were those falling between the 40th and 60th percentiles according to per capita giving.
107 contingency and the least inter cor relation with one another were selected as predictors for the instrument. These six variables were: campaign budget, campaign achievement, median family income, industrial index, in-migration and campaign volunteers.
The values of coefficients of contingency (C) and zero order cor relation of factors most highly associated with the criterion are given in Table 27.
Each variable category was assigned a numerical value equivalent to the proportion of communities in the high category
(see Table 28). The prediction score for a community was deter mined by adding the values of the six variable categories to which it was assigned. An experience table was then constructed from these data (see Table 29). In the same way as in Burgess method, the range of prediction scores was dichotomized in such a manner that greatest possible predictive accuracy could be attained (see
T able 30).
Predictive Instruments Constructed on I960 D ata
In addition to the sixteen predictive instruments constructed
on 1950 data, five more were constructed on the basis of I960
datar- three according to multiple linear regression method, one
for all communities 25,000 and over, and one for each size category
108 TABLE 2 7 . VALUES OP CHI SQUARE (X2 ) , COEFFICIENTS OF CONTINGENCY (C) AND OF ZERO ORDER CORRELATION ( r ) OF VARIABLES MOST HIGHLY ASSOCIATED WITH CRITERION, 1951
X2 c r V a r ia b le .Level o f V alue p V alue S i g n i f i can ce Campaign Budget 5 7 .3 .001 .476 .601 .01 Industrial Index 8 3 .3 .0 0 1 .438 .353 .0 1 Dwelling Conditions 6 7 .5 .001 .402 • 385 .01 W ell-to-do Families 5 9 .7 .001 .382 .432 .01 Poverty Index 5 8 .8 .001 .379 - .4 8 2 .0 1 Median Family Income 5 8 .2 .001 .377 .4 5 8 .0 1 Net Effective Buying Incom e 5 5 .4 .001 .369 • 374 .01 Campaign Volunteers 2 4 .3 .001 .328 .267 .0 1 Population Increase 3 1 -5 .001 .318 - .2 1 3 .01 Voting Behavior 2 8 .5 .001 .289 .425 .0 1 Campaign Achievement 3 1 .2 .001 .286 .361 .0 1 Geographical Location 3 1 .1 .001 .285 .319 .01 High School Enrollment 2 6 .3 .001 .264 .318 .01 White Collar Workers 2 4 .1 .001 .253 .190 .01 In-m igration 23.7 .001 .251 - .2 7 4 .01 Payroll Deductions 12.7 .0 0 1 .241 .299 .0 1 Population Size 19.3 .01 .227 .184 .01 Social Welfare Workers 8.7 .05 .221 • 304 .0 1
109 TABLE 2 8 . VARIABLES INCLUDED IN PREDICTIVE INSTRUMENT BASED ON GLUECK METHOD AND CATEGORIES? WITH HIGHEST AND LOW EST SCORES, 1951 (N =l85)
(0 denotes quintile or the fifth part of the distribution in which a given community falls)
Highest Score Lowest Score V a r ia b le C a te g o ry C a te g o ry Category Score Category S c o re Cam paign 390 & o v e r Under 150 (IQ) 15 B u d g et (5 0 ) 95 I n d u s t r i a l $3700 & over Under $2,100 In d e x (5 0 ) 73 ( 1 0 ) 13 Median Family $3,460 & over Under $2,800 Incom e (4 ,5 0 .) 66 (1 0 ) 10 Cam paign V o lu n te e rs 189 & over(50) 87 U nder 56 (10) 34 Cam paign Achievement 101-103$ (4Q) 73 Under 88$ (IQ) 23 In-m igration Under 3-7$(lQ) 74 9$ & over (50; 30
Range of Total Scores (Highest) 468 (Lowest) 125 a S c o re s of' categories between lowest and highest. 1. Campaign Budget 5• Campaign Achievement 150-190 (2Q) - 51 8 8 ^ -9 4 $ ( 2Q) = 50 2 0 0 -2 4 0 (3Q) = 60 9 5 $ -1 0 0 $ (3 0 ) = 55 2 50 -2 9 0 (4Q) = 85 104$ & over (*5Q) = 60
2 Industrial Index 6. In-migration $2100-2499 (2Q) = 33 6.4$-8 .9$ 2300-2 9 9 9 (3 0 ) = 62 5 .0 $ - 6 .3 $ 3000-3699 ( 4Q) = 70 3 .7 $ - 4 .9 $ 3 . Median Family Income $2800-3249 (2Q) = 46 3250-3 4 5 9 (3 0 ) = 63 4. Campaign Volunteers 56- 88 ( 2Q) = 48 89-116 (30) =48 1 17-188 (4 q ) = 71
110 TABLE 2 9 . PREDICTION SCORES PROM GLUECK INSTRUMENT AND PER CAPITA GIVING FOR 185 COMMUNITIES, 25,000 AND OVER, DICHOTOMOUS CRITERION, 1951
Per capita Giving P r e d ic tio n Borderline Communities S c o re s E x c lu d e d a All Communities N H igh Low N H igh Low 1 25-249 26 _ _ 26 28 M _ 28 250-2 9 9 17 4 13 23 7 16 300-314 4 2 2 11 4 7 315-324 5 4 1 12 9 3 325-349 20 15. 5 31 21 10 350-374 22 18 4 Z7 20 7 3 75-399 14 13 1 21 16 5 400-468 30 30 — 32 31 1 T o ta l 138 86 52 185 108 77
TABLE 3 0 . DICHOTOMY OP PREDICTION SCORES PROM GLUECK INSTRU MENT AND PER CAPITA GIVING: 185 COMMUNITIES, 2 5 ,0 0 0 AND OVERj DICHOTOMOUS CRITERION, 1951
Per capita Giving P r e d ic tio n Borderline Communities S c o re s Excluded3 All. Communities N H igh Low N H igh Low 125-3 1 4 47 6 41 62 11 51
315 -4 6 8 91 80 11 123 97 26 T o ta l 138 86 52 185 108 77
aBorderline communities were those falling between the 40th and 60th percentile according to per capita giving.
I l l Figure 8. Probability Values of Predictive Config urations Involving 100 Communities: Contiguous Categories, Dichotomous C riterion, 1961 Sample.
D Denotes Decile or the tenth Part of the D istri bution in which a Community Falls H Denotea Probability of High Giving L Denotes Probability of Low Giving
(N 100)
Median Family Income "U nder 3 rd D" " 3 rd D and o v e r" (Under $5,110) ($5110 and over) (N 20)a (N 80) .25H .75L .57H .43L43L
(See Figure 9)
a P r e d ic te d Low G iv in g
112 Figure 9* Probability Values of Predictive Config urations Involving Communities Having Median Family Income above the Third Decile ($5110 and over): Contiguous Cate gories, Dichotomous Criterion, 1961 Sam ple
D Denotes Decile or the Tenth Part of the Distribution in which a Community Falls H Denotea Probability of High Giving L Denotes Probability of Low Giving
M edian " 3 rd D 8c o v e r" F am ily ($5110 & over) Incom e (N 80) •57H .43L
Cam paign "U nder 3 rd "3rd D to 9th D" " 1 0 th D" B udget (Under 28^) ( 28 ^ - 44^) (45^ & over) (N 28) (N 34) (N 18) .18H .82L • 71H .29L .94H .0 6 l
Cam paign A c h ie v e "8th D & over' "Under 8th D" m ent ( 100$ & o v e r) (Under 100#) N 22) 1 N 1 2 ) . 92H______. 08L • 5 9 H ^ " \ . 4 lL
Lnanigration "7th D & over" "Under 7th D" ( 18 . 3# & o v e r) (U nder 1 8 . 3# ) (N 5) (N 17) . 20H .80L • 71H .29L
Cam paign "8th D & Over" "U nder 8 th D" V o lu n te e rs (178 & o v e r) (U nder 178) (N 7 ) (N 10) 1.00H .00L .50H .50L
113 25,000-99, 999 and 100,000 and over, and the other two were based on predictive configuration and Glueck approaches. The total num ber of communities used in the construction of these instruments was 100 (see Table 2); these were selected by disproportionate random method out of the 200 communities for vhich complete
responses to the questionnaire were received from the respective fund-raising federations. The methodology followed in the con
struction of the instruments was, for the most part, similar to that used for instruments described in the preceding section.
The predictive configuration instrument was constructed by using contiguous categories. Widespread categories type in
strument was not constructed for two reason; firstly, the sample
was small, and secondly, the instrument of this type constructed
on 1950 data had relatively low efficiency in comparison with other
instruments.
The probability values for "high" and "low" criterion result
ing from configuration of factors are presented in Figures 8 and 9.
The predictive configurations associated with "high" and "low"
categories, and their probability values are given in Table 31. It
will be seen from this table that for the 100 communities of which
actually 50 had "high" giving and the others "low", the outcome
predicted was high for 37 and low for 53, and the remaining 10 were
assigned to nonpredictable category.
114 The variables included in the prediction instrument based on
Glueck method and the scores for all the categories are presented in Table 32. It will be seen from this table that the range of total scores from the lowest possible to the highest was from 140 to
485. After scoring each of the 100 communities on the six varia bles according to the scores given in Table 32, an experience table for each size category was constructed, and the range of prediction scores was dichotomized in such a way that predictive accuracy was maximized. Thus it will be seen that the scores of
315, 325 and 310 respectively were used as the cutting points be tween the high and low giving for the three size categories--25, GOO-
49, 999, 50,000-99,999, and 100,000 or over (see Table 33).
The three instruments constructed according to multiple
linear regression method are presented in Tables 34, 35, and 36.
In selecting the variables for each instrument, the same procedure
as described in the preceding section was employed. Only those
variables were finally included whose t values were significant at
5 per cent level. It will be seen from these tables that the coef
ficients of Multiple Correlation (R) and Multiple Determination 2 (R ) for each of the equations are higher than those for the instru
ments constructed on 1950 data. It was probably because the data
obtained for I960, especially those from the questionnaire, were
115 relatively more accurate than for 1950.
As each variable was successively added, the coefficients 2 of Multiple Correlation (R) and Multiple Determination (R ) ob tained are given in Tables 37, 38 and 39 for each of the three
instruments. It is interesting to note that more than 91 per cent
of the variation in giving was associated with the 19 variables
used, that is, less than 9 per cent of the variation was due to
factors other than these variables. As a matter of fact, the co
efficient of determination was above 96 per cent for the size
category 100,000 and over; such a high value is seldom obtained
in social science studies. This probably reflects the accuracy
of the data and the judicious selection of variables.
V alid atio n
After having constructed the instruments, the next step was to
determine their predictive accuracy, efficiency and stability with a
view to making an objective comparison among them. This was done
by using the instruments based on 1950 data to predict the criterion
(giving) for three groups of com m unities--1951 sample of 200, 1961
sample I of 100 and 1961 Sample II of 50. The validity of instruments
constructed on I960 data was tested by using them on 1961 Sample I
and 1961 Sample II. The tests were made for total samples as well as
for each size category separately and also after excluding "Borderline
116 TABLE 3 1 . PREDICTIVE CONFIGURATIONS ASSOCIATED WITH HIGH AND LOW CATEGORIES, SIZE OF SUBSAMPLE AND PROBABILITY VALUES BASED UPON CONTIGUOUS CATE GORIES OF DICHOTOMOUS CRITERION, 1960 DATA, 1961 SAMPLE (N=100)
(D denotes decile or the tenth part of the distribution in which a given community fa lls)
Balance of the Subsample Total Communities Predictive Configurations'
Probability Probability N H igh Low N H igh Low
High Category
All communities 100 .5 0 .5 0 lb ; 2c 18 .9 4 .0 6 82 .4 0 .6 0 lb ; 2b; 3b 12 .9 2 .0 8 70 .31 .6 9 lb; 2b; 3a; 4a; 5b 7 1 .0 0 .00 63b .24 .76
Low C ategory
All communities 100 .50 .5 0 l a 20 .2 5 .7 5 80 .5 7 .4 3 lb ; 2a 28 .1 8 .8 2 52 .7 9 .21 lb; 2b; 3a; 4b 5 .2 0 .8 0 47° .8 5 .1 5
^Factor categories included in Predictive Configurations
1. Median Family Income 4. In-migration , a. Under $5,110 (Under 3rd D) a. Under 18.3% (Under 7th D) b. $5,110.and over (3rd D and over) b. 18.3% and over (7th D and o v er ) 2. Campaign Budget a. Under 28<* (Under 3rd D) 5. Campaign Volunteers b. 28£ - 44£ (3rd-9th D) a. Under 178 (Under 8th D) c . 45$ and over (10th D) b. 178 and over (8th D and o v er ) 3. Campaign Achievement a . Under 100% (Under 8 th D) b. 100% and over (8th D and over)
Includes 53 communities assigned to configurations predictive of Low Giving (See High Category section in the above Table) and 10 communities assigned to non-predictable category.
cIncludes 37 communities assigned to configurations predictive of High Giving (See High Category section in the above Table) and 10 communities assigned to non-predictable category. 117 TABLE 3 2 . VARIABLES INCLUDED IN PREDICTIVE INSTRUMENT BASED ON GLUECK METHOD AND CATEGORIES3 WITH HIGHEST AND LOWEST SCORES, 1961 SAMPLE (N=100)
(Q denotes quintile or the fifth part of the distribution in which a given community falls)
Highest Score Lowest Score V a r ia b le C ategory C ategory
C ategory Score Category Score
Campaign Budget $.45 and over (5Q) 95 Under $.23 (IQ) 5
I n d u s t r ia l Index $3920 and over (4,5Q) 75 Under $2620 (IQ) 25
Median Family Income $6430 and over (5Q) 90 Under $5110 (IQ) 20
Campaign V o lu n te e r s 216 and over (5Q) 75 Under 116 (1,2Q) 35
Campaign Achievement 101%, and over (5Q) 85 Under 91% (IQ) 30
In-migration Under 10.4%, (IQ ) 65 22.4%, and o v er (5Q) 25
Range of Total S co re s
Highest = 485 Lowest 140
aScores of categories between the Highest and Lowest.
1. Campaign Budget 4. Campaign Volunteers 23£ - 28£ (2Q) = 20 116 - 152 (3Q) = 45 29£ - 34£ (3Q) = 70 153 - 215 (4Q) = 60 35£ - 44
3. Median Family Income 6. In-migration $5110 - 5699 (2Q) = 40 10.4% - 14.2 (2Q) = 60 $5700 - 6039 (3Q) = 45 14.3% - 18.2 (3Q) = 60 $6040 - 6429 (4Q) = 55 18.3% - 22.3 (4Q) = 50 118 TABLE 3 3 . PREDICTION SCORES FROM GLUECK INSTRUMENT BASED UPON 1960 DATA AND PERCAPITA GIVING, BY SIZE OF COMMUNITY, DICHOTOMOUS CRITERION, 1961 SAMPLE, (N=100)
Per Capita Giving Prediction Scores and B o r d e r lin e Communi Size of Community ties Excluded a Total Sample N H igh Low N H igh Low
2 5 ,0 0 0 - 4 9 ,9 9 9 (N =20) Under 315b 6 1 5 6 1 5 315 and over 8 6 2 14 10 4 T o ta l 14 7 7 20 11 9 5 0 ,0 0 0 - 9 9 ,9 9 9 (N = 30) Under 325° 15 1 14 19 2 17 325 and over 10 9 1 11 9 2 T o ta l 25 10 15 30 11 19 100,000 and over (N = 50) Under 310b 20 2 18 24 3 21 310 and over 21 21 - 26 25 1 T o ta l 41 23 18 50 28 22
aBorderline communities were those falling between the 40th and 60th per centile according to per capita giving.
^These were used as the cutting points between the High and Low Giving on the principle of maximizing prediction accuracy.
119 TABLE 3 4 . COEFFICIENTS OF REGRESSION ( b ) , MULTIPLE CORRELATION ( R ), MULTIPLE DETERMINATION (R2 ) , *t* and *F* FOR VARIABLES INCLUDED IN MULTIPLE LINEAR REGRESSION EQUATION FOR COMMUNITY SIZE 2 5 ,0 0 0 AND OVER, BASED UPON 1960 DATA,(N=100)
V a r ia b le s L ev el *b’ ‘ t* Significance
Campaign Budget (X i) 6.1815 9.350 .001 Net Effective Buying Income (X2 ) 0 .1 4 2 5 3 .4 2 8 .001 Campaign Volunteers (X3) 0.1799 2.704 .01 Campaign Achievement (X4 ) 3 .1 3 1 8 2 .6 8 4 .0 1 Geographical Location (X5 ) -0 .0 2 8 5 -2 .4 8 3 .0 1 Industrial Index ( x 6 ) 0 .0 1 4 4 2 .4 3 9 .0 1 Payroll Deductions (X7 ) 0 .7 7 4 6 2 .2 0 1 .01 In-migration (X8 ) -2 .1 7 9 4 -2 .1 8 8 .01 Dwelling Conditions (X9 ) 3 .1 7 0 4 2 .0 6 1 .0 1
R = .876 R2 = .767 F = 32.933 (.001) bD = -566.34
The Regression Equation was
Y = -566.34 + 6.1815X! + 0.1425X2 + 0.1799X3 + 3.1318X4 -0.0285X5 + 0.0144X6 + 0.7746Xy -2.1794X8 + 3.1704X9
w here Y = Predicted Percapita Giving in cents X],— “ — X'9 = Independent Variables indicated above.
120 TABLE 3 5 . COEFFICIENTS OF REGRESSION ( b ) , MULTIPLE CORRELATION ( R ), MULTIPLE DETERMINATION (R2 ) , *t* and *F’ FOR VARIABLES INCLUDED IN MULTIPLE LINEAR REGRESSION EQUATION FOR COMMUNITY SIZE 2 5 ,0 0 0 - 9 9 ,9 9 9 . BASED UPON 1960 DATA (N=50)
L e v el o f V a r ia b le s *b* *t* Significance
Campaign Budget (XI) 6 .2 6 5 3 6 .6 0 9 .001 Campaign Volunteers (x2) 0 .4 1 7 3 4 .4 2 5 .001 In-migration (x3) -6.8338 -3.954 .001 Geographical Location (x4) -0 .0 6 0 2 - 3 .2 3 9 .01 Median Family Income (x5) 0 .1 4 4 5 3 .0 4 5 .0 1 Dwelling Conditions R = .901 R2 = .812 F= 22.171 (.001) bQ = -1 1 2 4 .1 0 The Regression Equation was Y= -1124.10 + 6.2653X1 + 0.4173X2 - 6.8338X3 -0.0602X4 + 0.1445X5 +' 7.2255X6 + 12.7150Xy + 0 . 9150Xg where Y = Predicted Percapita Giving in cents Xj——Xs = Independent Variables Indicated above. 121 TABLE 3 6 . COEFFICIENTS OF REGRESSION ( b ) , MULTIPLE CORRELATION ( R ) , MULTIPLE DETERMINATION (R2 ) , *t* AND ’F ’ FOR VARIABLES INCLUDED IN MULTIPLE LINEAR REGRESSION EQUATION FOR COMMUNITY SIZE 1 0 0 ,0 0 0 AND OVER. BASED UPON 1960 DATA (N=50) L e v e l o f V a r ia b le s *b* •t* Significance Campaign Budget (xx) 7 .9 1 1 9 1 0 .4 6 6 .001 Net Effective Buying Income (x2) 0 .2 3 5 9 5 .7 5 6 .001 Dwelling Conditions (x3) -4.3927 - 3.211 .001 Median School Years Completed (x4) 2 1 .7 0 3 0 2 .5 7 9 .01 Payroll Deductions (X5 ) 0.7952 2.112 .0 1 Population Size(in Thousands) (x6) 0 .0 2 4 1 1 .7 8 1 .0 5 R = .934 R2 = .872 F = 48.931 (.001) bQ = -248.05 The Regression Equation was Y = -248.05 + 7.9119X! + 0.2359X2 - 4.3927X3 ♦21.7030X4 + 0.7952X5 + 0.024IX6 where Y = Predicted Percapita Giving in cents X^ X6 = Independent Variables Indicated above. 122 TABLE 3 7 . VARIABLES PROGRESSIVELY INCLUDED AND THE RESULTING COEFFICIENTS OF MULTIPLE CORRELATION (R ), MULTIPLE DETERMINATION (R2 ) AND *F* RATIOS FOR MULTIPLE LINEAR REGRESSION INSTRUMENT FOR COMMUNITY SIZE 2 5 ,0 0 0 AND OVER, BASED UPON 1960 DATA, (N=100) Variables progressively L e v e l o f included and - R R2 *F* Significance Used in the Instrument Campaign Budget .7 5 1 .5 6 4 In-migration .7 6 1 .5 7 9 6 6 .7 3 .0 0 1 Campaign Volunteers .774 .5 9 9 4 7 .8 0 .0 0 1 Payroll Deductions .7 8 5 .6 1 7 3 8 .2 0 .0 0 1 Campaign Achievement .7 9 9 .638 3 3 .0 9 .0 0 1 Geographical Location .799 .6 3 8 2 7 .2 8 .0 0 1 Net Effective Buying Income .8 6 0 .7 4 0 3 7 .4 0 .0 0 1 Dwelling Conditions .867 .752 34.43 .001 Industrial Index .876 .767 32.93 .001 Not Used in the Instrument White Collar Workers .883 .779 31.39 .001 High School Enrolment Population Increase Women Employed Unemployment Median School Years Completed W ell-to-do Families Population Size Median Family Income Poverty Index .8 8 7 .788 15.61 .001 TABLE 3 8 . VARIABLES PROGRESSIVELY INCLUDED AND THE RESULTING COEFFICIENTS OF MULTIPLE CORRELATION (R ) , MULTIPLE DETERMINATION (R2 ) AND ’ F* RATIOS FOR MULTIPLE LINEAR REGRESSION INSTRUMENT FOR COMMUNITY SIZE 25,000 - 99,999, BASED UPON 1960 DATA, (N=50) Variables progressively- L ev el o f included and - R R2 tp« Significance Used in the Instrument Campaign Budget .7 8 5 .665 Campaign Volunteers .8 3 4 .696 5 3 .7 4 .001 Median Family Income .8 4 0 .7 0 5 3 6 .6 8 .001 Geographical Location .842 .709 2 7 .3 9 .0 0 1 Dwelling Conditions .8 5 4 .7 2 9 2 3 .6 2 .001 Poverty Index .856 .733 19.64 .001 Payroll Deductions .861 .741 17.14 .001 In-migration .901 .8 1 2 2 2 .1 7 .001 Not Used in the Instrument Women Employed .905 .819 2 0 .1 0 .001 Industrial Index .9 0 8 .8 2 4 1 8 .3 3 .001 Campaign Achievement High School Enrolment Population Increase Net Effective Buying Income Unemployment White Collar Workers Median School Years Completed W ell-to-do Families Population Size .9 1 5 .8 3 8 8 .1 5 .001 124 TABLE 3 9 . VARIABLES PROGRESSIVELY INCLUDED AND THE RESULTING COEFFICIENTS OF MULTIPLE CORRELATION ( R ) , MULTIPLE DETERMINATION (R2 ) AND *F* RATIOS FOR MULTIPLE LINEAR REGRESSION INSTRUMENT FOR COMMUNITY SIZE 1 0 0 ,0 0 0 AND OVER, BASED UPON 1960 DATA, (N=50) L ev el o f Significance Used in the Instrument Campaign Budget .7 9 7 .6 3 8 Net Effective Buying Income .901 .812 1 0 1 .4 3 .0 0 1 Population Size .906 .821 7 0 .3 7 .001 Payroll Deductions .911 .830 54.81 .001 Dwelling Conditions .923 .852 5 0 .8 6 .0 0 1 Median School Years Completed .934 .872 48.93 .001 Not Used in the Instrument High School Enrolment .9 3 8 .8 8 0 4 3 .8 6 .001 Geographical Location .946 .894 43.27 .001 Campaign Volunteers .946 .8 9 5 3 7 .9 1 .001 Population Increase .956 .913 40.92 .001 Campaign Achievement Median Family Income Poverty Index In-migration Women Employed Unemployment White Collar Workers W ell-to-do Families Industrial Index .963 .927 19.97 .001 125 Communities. " The Borderline Communities were those falling between the 40th and 60th percentile with respect to per capita giving. Predictive Configuration, Burgess and Glueck Instruments One of the major problems that had to be countered in this process stemmed from the fact that the values or figures for a number of variables used had changed considerably during the period 1950 to I960; for example, the Median Family Income for the country, as a whole, had gone up from $3,073 in 1949 to $5,417 in 1959. Thus, it is obvious that predictive configurations or critical categories based on data for 1950, especially those in volving values in dollars, could not be used in their original form for the validation samples of 1961. It is important to describe briefly the procedure adopted to meet this problem. While breaking the variables into categories for the purpose of constructing the instruments, the aim was to do so in such a maimer that the same terminology of the categories as stated ori ginally might be employed when using them on subsequent samples. This was accomplished by categorizing each variable both in term s 20 of its absolute value as well as deciles and quintiles. In fact 20 This was suggested by the w riter's adviser Professor M erriss Cornell. the categorization of absolute values was made on the basis of deciles or quintiles. Such categories for one of the predictor's median family income based on 1950 data are presented in Table 3. This procedure facilitated the use of the same terminology for the various categories of the variables when applying them on subse quent samples. Thus, for example, in using the configurational instrument presented in Table 4 to predict the outcome for communities in cluded in 1961 sample, the absolute values of the categories of each factor were converted in term s of the corresponding figures for I960. The process of converting the categories of one of the variables--median family income—was as follows: 1950 1960 Median Family Income Median Family Income (a) Under 3rd Decile (under (a) Under 3rd Decile $2,800) (under $5,100) (b) 3rd-5th Decile ($2,800- (b) 3 rd - 5th D ecile $3,329) ($5, 100-5,699) (c) 6th Decile and over (c) 6th Decile and over ($3, 330 & over) ($5,700 & over) Thus when using the instrument on 1961 sample even though the original categories--under 3rd decile, 3rd-5th decile and so on were used, still their cutting points were taken as $5, 100 and 5, 700 instead of $2, 800 and 3, 329. The underlying assumption was that the nature and scope of the distribution of the variables included 127 had not significantly changed during the interval. It will be thus seen that in the very process of construction of the instruments, there was a built-in potentiality for appropriate adjustment, necessitated for using them on subsequent samples. This process facilitated the validation of all instruments ex cept those based on multiple linear regression method. For the latter instruments, a somewhat different process had to be utilized. Multiple Linear Regression Instruments As mentioned in the preceding chapter, a multiple linear re gression equation may be expressed as follows: Y= b + b .X , + b 0X_------+ b X where Y is the o 1 1 2 2 I I predicted outcome, and bQ, b^, b^ are constants that reveal the relation of the dependent variable Y to independent variables X,. x 2— -xr Now if it can be assumed, that in I960 the dependent variable (giving) was related to the independent variables X , X X in 1 u X a sim ilar manner as it was ten years back, then it would be quite 21 safe to use the values of "b's" as obtained from 1950 data for predicting the outcome for 1961 sample. This assumption would be invalidated, if the "b's" derived from 1950 data do not enable one 21 The "b's" include b,, b_----b, and not b . 1 2 1 o 128 to predict the outcome for the later sample with a reasonable degree of accuracy. However, if it can be shown that the accuracy of out come predicted on the basis of "b's" derived from 1950 data is comparable to that from the "b's" obtained from I960 data, then the assumption would not be unreasonable. It will be seen in the next chapter that the prediction results obtained by using the b's derived from 1950 were comparable to those derived from I960 data; the assumption was thus, probably, valid. The value of b o , however, needs some modification, since it is dependent upon the values of the means of the variables; some of which, at least, have changed considerably (see Table 40). Therefore, it seems quite logical that if a regression equa tion based on 1950 data is to be used to predict outcome for 1961 sample, the value of its bQ should be adjusted in such a way that the changes in the means of the variables might be accounted for. This was done by making use of the usual equation for deriving b^, which may be stated as follows: b0 = Y - b1k 1 - b2X2 - b ^ ...... b ^ . where Y is the mean of the dependent variable, and X j, X^— ---- X^. are the means of the respective independent variables X^, X ^ --X^.. In order to obtain the modified value of bQ, the means of each 129 TABLE 40. MEANS AND STANDARD DEVIATIONS OF SELECTED VARIABLES USED IN CONSTRUCTION OF INSTRUMENTS, BY SIZE OF COMMUNITY Means and Standard D eviations Based on - 1950 Data 1960Data 25,000-49,999 50,000-99,999 100,000 and 25,000-99,999 100,000 and over over Mean S. D. Mean S. D. Mean S. D. Mean S. D. Mean S. D. Per Capita Giving 2.46 1.05 2.36 0.88 2.45 0.97 3.83 1.56 4.10 1.23 Campaign Budget 27.3 15.7 21.9 .8.1 21.0 7.5 35.5 14.5 30.6 10.2 Campaign Achievement 96.6 7.0 93.6 9 .0 96.4 7.4 94.5 7.6 96.3 4 .9 Payroll Deductions 15.5 16.2 17.6 17.4 21.8 17.7 45.8 21.2 54.6 20.7 Campaign V olunteers 128 86 136 154 127 76 156 118 163 115 Median Family Income 3286 488 3367 576 3323 440 5774 811 5788 707 Poverty Index 22.2 9.8 22.1 9.2 22.6 7.8 17.8 7.2 18.3 6.3 In^migration 6.2 3 .4 5.9 3.0 5.8 2.7 16.2 7.7 18.6 8.4 Geographical Location 3114 454 3119 459 2961 573 5613 892 5236 944 High School Enrolment 87.6 6.1 87.1 5.2 86.3 4.4 89.0 4 .5 87.4 3.5 Population Increase 20.4 22.8 24.2 32.4 27.6 32.8 17.8 14.7 37.9 57.7 Women Employed 33.5 4.1 32.8 5.0 33.4 3.7 36.8 5.3 37.4 3.8 Dwelling Conditions 73.5 14.8 74.0 12.1 72.3 12.9 78.3 6.7 79.1 6 .5 Net E ffe c tiv e B. Income 1440 229 1470 239 1530 244 1946 253 1996 244 Unemployment 4.5 1.9 1.4 4.7 1.6 5.6 1.7 4.9 1.5 , 4 - 3 White Collar Workers 41.8 8.2 42.2 7.9 86.3 4 .4 41.6 7.2 43.6 6.0 Industrial Index 3075 918 3073 1108 3352 1378 3628 1221 3913 1594 Median School Years Completed 10.1 1.3 10.0 1.6 10.2 1.2 10.7 1.2 11.0 0.9 Well to do Fam ilies 21.2 5.7 22.8 8.0 22.8 5.6 14.7 6.8 14.7 3.7 variable as obtained from i960 data were used in this equation, while the b*s remained the same. For example, the new value of bQ for the regression equation given in Table 16 for community size 50,000-99,999 was obtained as follows: bD = -5.8234 (35.52) - (-7.5490) (1 7 .8 0 ) -1.2803 (5.64) - 12.5860 (5.6 0 ) -2.6825 (27.50) - (-0.0493) (5613.00) -(-3.7252) (78.30) = 663.5OO It will be seen that the original value of b in that equa- o tion was 535*460. The increase from 535*460 to 663*500 reflects the change in the magnitudes of the corresponding variables. Thus, the equation when used for the validation samples of 1961 was Y = 663.5OO + 5.8234 X1 - 7.5490X2 + 1.2803X3 + 12.5860 X^ + 2.6825X - 0.0493X, - 3.7252X . 5 6 7 Similarly, the equations presented in Tables 15 and 17 which were derived from 1950 data for population categories, 25,000- 49,999, an dict the outcome for 1961 samples. These became as follows: For community size 25,000-49,999 *• Y = -745.000 + 3.9221X i + 6 .7672 X2 - 0.9824X^ +4.8269x^ + o.o 497 X5 + o . i 444Xg + i .478 tx t 131 For community size 100,000 and over Y = -252, 1 + 5.4415X - 13.3350X + 3.8220X 1 2 3 + ,3550X4 + 22.720X5 In respect to the equation for community size 100, 000 and over, another problem was encountered. This stemmed from the fact that a major change had occurred, from 1950 to I960, in the definition of "in-migration" as given by the United States Bureau of the Census. In the 1950 census, the figures on this variable represented the number of inhabitants one year old and over who were living in a different county or abroad one year prior to the date of enumeration. In the census of I960, however, these figures included persons five years old and over who lived in different counties in the United States in 1955 and I960. Since, inthe case of I960 census the in-migration data included figures for two years instead of one, it was considered logical, when using the equation on 1961 sample, to reduce the value of that variable to one-half both in computing the new value of bQ as well as in predicting out co m e. Another problem encountered in the process of validation was due to the fact that for the community size 50, 000-99, 999, one of the predictors included in the regression equation was "dwelling modernity" for which no comparable data was available 132 from I960 census reports. The problem was met by using instead the figures obtained for "dwelling conditions"; the two variables were highly correlated (. 778), as determined on the basis of 1950 data (see Table 43). Also, for a few communities included in the 1961 samples, the data on "dwelling conditions" were not available at the time of validation. So it was deemed necessary to use 1950 data for such communities; however, a "correction factor" was used to mitigate the problem emanating from the changes in the index during the course of ten years. The "correction factor" was evolved by determining the net increase in the value from 1950 to I960 for each group of communities falling in different categories according to the variable. Thus the values of the correction factor for com munities falling in the categories under 50 per cent, 50.0-69.9 per cent, 70.0-79.9 per cent, 80.0-89.9 per cent, 90.0-94.9 per cent and 95. 0-96. 9 per cent were 10.0, 8.5, 7.4, 6.5, 5.5, 2.4 and 0.5 respectively. The methodology followed and procedures adopted in the pro cess of construction and validation of instruments according to the four methods have been discussed in this chapter. The instruments that were developed have also been presented. In the chapter that follows, the validation results and other findings of the research are discussed. 133 CHAPTER IV RESEARCH FINDINGS AND VALIDATION One of the primary objectives of the present study was to determine whether, and if so, how far it is feasible to develop valid, efficient and stable instruments to predict community per formance. To the best of this writer’s knowledge, the prediction of a phenomenon with respect to a community is hitherto almost an uncharted field. The various prediction methods developed so far have been employed, by and large, for forecasting the performance or behavior of individuals. The twenty-one instruments described and presented in the preceding chapter were designed to predict the per capita giving to the community fund-raising federation. Three hundred and fifty communities each having a population of 25,000 or more were used in constructing sixteen instruments based on 1950 data. Five more instruments were constructed by using the data for i960 for a sam ple of 100 communities. This sample was also used for validation of the instruments constructed on 1950 data. Another 1961 sample of 50 communities was selected for the validation of both 1950 as well as i960 instruments. 13^ This chapter deals with the findings on each of the three Hypotheses already presented. The first hypothesis postulated the relationship of various social and economic factors with per capita giving. The second and third hypotheses involved the con struction of instruments based on four major approaches and the determination of their relative accuracy, efficiency, and stability. Hypothesis I--Certain social and economic character- • istics of a community are related to the "higher" or "lower" per capita giving. In Tables 41 and 42, the zero order correlations with per capita giving of all the independent variables used in the study are presented by size of community for the 1951 and 1961 samples. It will be seen from these tables that seven variables, median fami ly income, poverty index, well-to-do-families, net effective buying income, geographical location, campaign budget, dwelling condi tions are significantly correlated (5 per cent level) with the criterion; this is true for each of the size categories for both the samples. Some other variables that are significantly correlated with the per capita giving in more than one population category of both the samples are--campaign volunteers, payroll deductions, campaign achievement, industrial index, high school enrollment and in-migration. It will also be seen that a few variables in- 135 TABLE 41. ZERO ORDER CORRELATIONS OF INDEPENDENT VARIABLES USED IN THE STUDY WITH PERCAPITA GIVING, BY SIZE OF COMMUNITY, 1950 DATA S ize of Community 25,000 and over 25,000 - 49,999 50,000 - 99,999 100,000 and ovei Independent (N==350) (N=134) (N=98) (N=118) V ariables Level of Level of Level of Level of r S ig n if i r S ig n if i r S ig n if i r S ig n if i cance cance cance cance Population Size .184 .01 -.0 1 7 N.S -.0 0 5 N.S .298 .01 o 1—« Median Family Income .458 .01 .459 .01 .346 .01 .581 • o i—1 Poverty Index -.482 • -.4 1 2 .01 -.4 5 1 .01 -.5 8 0 .01 Well-to-do Families .432 .01 .473 .01 .287 .01 .586 .01 Non-White Population -.265 .01 -.217 .05 -.2 2 4 .05 -.3 5 0 .01 In-migration -.2 7 4 .01 -.207 .05 -.3 7 4 .01 -.2 6 9 .01 Productive Population -.0 5 9 N.S .261 .01 .130 N.S .269 .01 Craftsmen .156 .01 .154 N.S .122 N.S .222 .05 Unskilled Workers -.1 2 3 .05 -.0 1 9 N.S .036 N.S -.3 1 8 .01 White Collar Workers .190 .01 .156 N.S .052 N.S .326 .01 Unemployed -.0 9 1 N.S -.0 0 9 N.S -.0 4 4 N.S -.1 9 9 .05 Health Index -.1 9 1 .01 -.1 5 8 N.S .014 N.S -.4 3 4 .01 Dwelling Conditions .385 .01 .401 .01 .279 .01 .444 .01 Dwelling Modernity .416 .01 .451 .01 .343 .01 .443 .01 High School or More Education .163 .01 .162 N.S .046 N.S -.4 2 4 .01 Less Than 5 Grades o 1—» Education -.285 • -.2 8 6 .05 -.0 8 1 N.S -.4 2 4 .01 High School Enrollment .318 .01 .389 .01 .102 N.S .426 .01 Median School Years Completed .137 .05 .194 .05 -.0 9 9 N.S .306 .01 Industrial Index .353 .01 .209 .05 .163 N.S .535 .01 o »—» Campaign Achievement .361 .01 .215 .05 .355 • .473 .01 Net Effective Buying Income .374 .01 .325 .05 .323 .05 .450 .01 Population Increase -.214 .01 -.2 5 2 .01 -.1 9 2 N.S -.2 2 1 .05 TABLE 41 (CONTD.) S ize of Community 25,000 and over 25,000 - 49,999 50,000 - 99,999 100,000 and over Independent (N=350) (N=134) (N=98) (N=118) V ariables Level of Level of Level of Level of r S ig n if i r S ig n if i r S ig n if i r S ig n if i cance cance cance cance Women Employed .127 .05 .195 .05 .274 .01 -.0 6 0 N.S Geographical Location .319 .01 .422 .01 .229 .05 .330 .01 (N==252) Crime Index .092 N.S Juvenile Delinquency -.0 3 6 N.S Public Welfare Expendi ture .056 N.S Government Expenditure on H ealth and H osp itals .082 N.S Government Taxes .108 N.S Voting Behavior .374 .01 .387 .01 .439 .01 (N=a 85) (N=51) (N==56) (N=78) Social Welfare Workers .304 .01 .141 N.S .355 .01 Payroll Deductions .299 .01 .138 N.S .364 .01 .370 .01 Campaign V olunteers .267 .01 .202 N.S .171 N.S .514 .01 Campaign Budget .582 .01 .681 .01 .576 .01 .636 .01 OJ TABLE 42. ZERO ORDER CORRELATIONS OF INDEPENDENT VARIABLES USED IN THE STUDY WITH PERCAPITA GIVING, BY SIZE OF COMMUNITY, 1960 DATA CORRELATION WITH PERCAPITA GIVING AND SIZE OF COMMUNITY Independent 25,,000 25 ,000 - 100,000 V ariables and over 99,999 and over Level of Level of Level of r S ig n if i r S ig n if i r S ig n if i cance cance cance N==100 N=50 N=50 Population Size' .168 N.S -.0 9 2 N.S .235 N.S Median Family Income .513 .01 .474 .01 .574 .01 Poverty Index .424 .01 -.367 .01 -.521 .01 W ell-to-do Families .435 .01 .381 .01 .589 .01 In-migration .233 .05 -.082 N.S -.4 5 3 .01 White Collar Workers .189 N.S .233 N.S .094 N.S Unemployment .181 N.S -.1 6 4 N.S -.1 6 8 N.S Dwelling Condi tions .334 .01 .343 .05 .318 .05 High School Enrollment .327 .01 .298 .05 .447 .01 Median School Yrs. Completed .246 .05 .258 N.S .197 N.S Industrial Index .392 .01 .240 N.S .553 .01 Campaign Achieve ment .323 .01 .403 .01 .144 N.S Net Effective Buying Income .513 .01 .388 .01 .673 .01 Population Increase-. 184 N.S .003 N.S -.356 .01 Women Employed .068 N.S -.022 N.S .211 N.S Geographical Lo cation .264 .01 .270 .05 .322 .05 Payroll Deductions .252 .05 .138 N.S .373 .01 Campaign Volunteers .362 .01 .482 .01 .205 N.S Campaign Budget .751 .01 .785 .01 .797 .01 138 eluding crime index, juvenile delinquency, government expenditure on public welfare, government expenditure on health and hospitals, and government taxes had a very insignificant correlation with giv ing. It was also found in the process of Multiple Hypotheses de scribed in the preceding chapter, in connection with the development of instruments based on multiple linear regression method, that the exclusion of these variables did not significantly reduce the co- 2 efficient of Multiple Determination (R ). It was for this reason that these were dropped and not used in subsequent analysis. It will be seen that excluding these five variables there is hardly any other variable which is not significantly correlated with giving in one or the other size categories, in either of the samples. In Tables 43 and 44, all possible intercorrelations with one another of the variables» including the dependent (per capita giving)* are presented. From these tables, it is evident that median family income, poverty index, we 11-to-do-fam ilies, dwelling conditions, dwelling modernity are highly intercorrelated. This was the reason why not more than one or two of these could be included in any pre dictive instrument, excepting the Burgess type, although they were all highly related with giving. Some variables even though highly correlated with giving were not significantly intercorrelated with each other and so may be considered as "independent" of each other. 139 TABLE 43. CORRELATION MATRIX OF ALL VARIABLES USED FOR CONSTRUCTING INSTRU MENTS BASED UPON 1950 DATA, COMMUNITY SIZE 25,000 AND OVER (N=350)a Variables 1 2 3 4 5 6 7 1. Per Capita Giving 1.000 .184 .45,8 -.4 8 2 .432 -.2 6 5 -.2 7 4 2. Population Size 1.000 .135 -.1 2 7 .173 .086 -.1 3 4 3. Median Family Income 1.000 -.930 .931 -.5 0 0 -.0 9 9 4. Poverty Index 1.000 -.7 6 9 .613 .196 5. Well-to-do Families 1.000 -.2 8 0 -.0 1 3 6. Non-white population 1.000 .172 7. In-migration 1.000 8. Productive Population 9. Craftsmen 10. Unskilled Workers 11. White C ollar Workers 12. Unemployment 13. Health Index 14. Dwelling Conditions 15. Dwelling Modernity 16. High School or More Educ. 17. Less than 5 Grades Educ. 18. High School Enrollment 19. Median School Yrs. Compd. 20. Industrial Index 21. Campaign Achievement 22. Net Effective Buying Inc. 23. Population Increase 24. Women Employed 25. Geographical Location 26. Crime Index 27. Juvenile Delinquency 28. Public Welfare Expend. 29. Govt. Expend, on Health and H o sp ita ls 30. Government Taxes 31. Voting Behavior 32. Social Welfare Workers 33. Payroll Deductions 34. Campaign V olunteers 35. Campaign Budget 140 TABLE 43 (CONTD.) V ariables 8 9 10 11 12 13 14 1. Per Capita Giving -.0 6 9 .156 -.1 2 3 .190 -.0 9 1 -.1 9 1 .385 2. Population Size .219 .086 .041 . .076 .068 -.0 5 5 .129 3. Median Family Income .423 .366 -.431 .540 -.1 4 1 -.4 0 3 .780 4. Poverty Index -.3 9 2 -.486 .287 -.402 .123 .420 -.7 6 8 5. Well-to-do Families .471 .198 -.4 7 8 .609 -.1 5 3 -.3 3 3 .723 6. Non-white Population .002 -.2 9 4 .248 -.1 6 0 .103 .323 -.4 2 9 7. In-migration .121 -.2 3 7 -.3 2 6 .328 -.1 6 5 .119 -.0 6 9 8. Productive Population 1.000 -.0 0 5 -.1 3 9 .451 -.1 1 8 -.1 3 1 .444 9. Craftsmen 1.000 -.0 3 1 -.1 2 3 .102 -.176 .370 10. Unskilled Workers 1.000 -.752 .227 .218 -.3 9 4 11. White Collar Workers 1.000 -.1 0 8 -.1 6 0 .580 12. Unemployment 1.000 .070 .071 13. Health Index 1.000 -.3 8 3 14. Dwelling Conditions 1.000 15. Dwelling Modernity 16. High School or More Educ. 17. Less than 5 Grades Educ. 18. High School Enrollment 19. Median School Yrs. Compd. 20. Industrial Index 21. Campaign Achievement 22. Net Effective Buying Inc. 23. Population Increase 24. Women Employed 25. Geographical Location 26. Crime Index 27. Juvenile Delinquency 28. Public Welfare Expend. 29. Govt. Expend, on H ealth and H o sp ita ls 30. Government Taxes 31. Voting Behavior 32. Social Welfare Workers 33. Payroll Deductions 34. Campaign V olunteers 35. Campaign Budget 141 TABLE 43. (CONTD.) V ariables 15 16 17 18 19 20 21 1. Per Capita Giving .416 .163 -.2 8 5 .318 .137 .353 .361 2. Population Size .109 .031 .004 .010 -.010 .263 .041 3. Median Family Income .802 .626 .114 .271 .063 .690 .031 4. Poverty Index -.8 5 4 -.4 9 3 .692 -.603 -.087 -.321 -.0 8 2 5. Well-to-do Families .669 .645 .532 .108 .227 .031 .713 6. Non-white Population -.6 0 2 -.2 7 3 .633 -.4 1 4 -.1 1 4 -.0 9 3 -.0 3 8 7. In-migration -.236 .344 -.017 -.157 .087 -.1 4 9 -.0 6 7 8. Productive Population .421 .351 -.158 .182 .042 .305 -.0 1 3 9. Craftsmen .391 -.0 1 9 -.2 6 2 .238 -.026 .010 .040 10. U nskilled Workers -.3 2 3 -.716 .501 -.406 -.235 -.0 9 1 -.0 5 3 11. White Collar Workers .400 .807 -.4 1 9 .421 .199 .273 .011 ,12. Unemployment -.1 2 6 -.2 1 3 .234 -.0 0 9 -.0 7 7 -.0 1 8 -.1 2 1 13. Health Index -.4 5 8 -.2 9 6 .475 -.4 2 3 -.1 1 9 -.0 1 8 -.0 3 5 14. Dwelling Conditions .778 .604 -.559 .623 .094 .228 .003 15. Dwelling Modernity 1.000 .476 -.6 6 0 .558 .094 .247 .078 16. High School or More Educ. 1.000 -.6 1 6 .562 .202 .154 .016 17. Less than 5 Grades Educ. 1.000 -.640 -.214 -.197 -.052 18. High School Enrollment 1.000 .169 .138 .001 19. Median School Yrs. Compd. 1.000 .041 -.101 20. Industrial Index 1.000 .117 21. Campaign Achievement 1.000 22. Net Effective Buying Inc. 23. Population Increase 24. Women Employed 25. Geographical Location 26. Crime Index 27. Juvenile Delinquency 28. Public Welfare Expend. 29. Govt. Expend, on H ealth and H osp itals 30. Government Taxes 31. Voting Behavior 32. Social Welfare Workers 33. Payroll Deductions 34. Campaign V olunteers 35. Campaign Budget 142 TABLE 43 (CONTD.) V ariables 22 23 24 25 26 27 28 1. Per Capita Giving .374 -.2 1 4 .127 .319 .092 -.1 3 3 .056 2. Population Size .174 -.007 .044 .205 -.051 .027 .169 3. Median Family Income .194 .031 .194 .425 -.0 0 7 -.1 7 7 .001 4. Poverty Index -.5 9 6 .030 -.178, -.453 -.010 .228 -.0 6 2 5. Well-to-do Families .078 .078 .272 .387 -.0 5 5 -.1 0 1 -.0 7 2 6. Non-white Population -.2 2 2 .177 .229 -.182 -.073 .183 -.1 8 0 7. In-migration -.0 4 2 .563 -.008 -.285 .182 .517 -.1 4 1 8. Productive Population .446 .097 .506 .295 -.0 3 3 -.0 0 2 -.0 7 3 9. Craftsmen .092 .106 -.2 7 3 .107 .051 -.2 1 5 -.1 1 3 10. Unskilled Workers -.4 3 4 -.2 2 1 .146 -.0 5 5 -.1 2 6 -.2 3 6 .216 11. White Collar Workers .624 .200 .319 .295 .087 .304 -.1 2 3 12. Unemployment -.0 3 6 .061 -.025 -.005 -.0 0 0 -.0 4 8 .196 13. Health Index -.2 9 6 .075 .003 -.1 2 2 .167 .257 -.1 2 2 14. Dwelling Conditions .661 .083 .195 .440 .002 -.0 9 3 .113 15. Dwelling Modernity .606 -.0 3 3 .145 .417 -.059 -.243 .120 16. High School or More Educ. .596 .238 .203 .213 .098 .152 .005 17. Less than 5 Grades Educ. -.4 6 2 .040 .046 -.2 4 5 -.0 2 1 .112 -.0 2 1 18. High School Enrollment .477 - . 041 -.0 0 7 .297 .003 -.2 4 2 -.0 3 6 19. Median School Yrs. Compd. .140 .009 -.0 0 4 .013 .078 .204 .064 20. Industrial Index .347 -.2 0 3 .389 .447 -.072 .072 -.0 4 8 21. Campaign Achievement -.0 0 7 -.0 4 2 .072 .322 .025 -.054 -.1 4 8 22. Net Effective Buying Inc. 1.000 .005 .301 .452 .006 .060 -.190 23. Population Increase 1.000 -.0 7 3 -.1 8 8 .042 .132 -.1 2 2 24. Women Employed 1.000 .321 -.2 1 7 .039 .085 25. Geographical Location 1.000 -.0 1 7 -.3 4 2 .151 26. Crime Index 1.000 .150 -.0 4 6 27. Juvenile Delinquency 1.000 -.0 3 0 28. Public Welfare Expend. 1.000 29. Govt. Expend, on Health 30. Government Taxes 31. Voting Behavior 32. S o c ia l W elfare Workers 33. Payroll Deductions 34. Campaign V olunteers 35. Campaign Budget 143 TABLE 43 (CONTD.) V ariables 29 30 31 32 33 I'.'. 35 1. Per Capita Giving .119 .108 .425 .304 .403 .243 .555 2. Population Size .132 .221 .106 .259 .180 .041 -.0 1 3 3. Median Family Income -.074 .015 .622 .251 .357 .196 .310 4. Poverty Index .085 -.065 -.694 -.2 1 8 -.3 6 0 -.2 1 4 -.2 8 7 5. Well-to-do Families -.0 6 3 -.0 0 5 .468 .235 .342 .165 .281 6. Non-white Population -.0 1 0 -.1 3 7 -.7 9 1 -.2 3 7 n. I l l -.1 2 3 -.3 6 7 7. In-migration -.085 -.280 -.385 .001 -.380 -.1 9 2 -.1 2 2 8. Productive Population -.1 0 4 -.0 4 7 -.0 2 6 .014 -.2 1 7 .040 .058 9. Craftsmen -.131 -.0 1 3 .315 -.0 8 0 .309 -.0 1 6 .083 10. Unskilled Workers .166 .217 .261 .448 .083 -.0 4 5 -.2 5 9 11. White Collar Workers -.1 1 7 -.1 6 1 .213 .383 -.1 8 5 .085 .216 12. Unemployment .048 .292 .067 .045 .101 -.082 -.103 13. Health Index .046 -.140 -.556 -.226 -.3 1 2 -.1 9 1 -.2 6 0 14. Dwelling Conditions -.0 2 8 .287 .617 .351 .210 .216 .302 15. Dwelling Modernity .018 .193 .707 .199 .215 .168 .218 16. High School or More Educ.-.112 -.180 .342 .445 -.0 2 7 .158 .222 17. Less than 5 Grades Educ. .077 .055 -.684 -.331 -.114 -.1 7 1 -.2 0 5 18. High School Enrollment -.0 4 9 .107 .560 .240 .240 .288 .303 19. Median School Yrs. Compd. -.057 -.137 .316 .419 -.1 1 1 .148 .239 20. Industrial Index .006 -.000 .320 .226 .061 .201 .291 21. Campaign Achievement .063 -.0 8 3 -.0 4 6 -.0 2 8 .143 -.0 1 5 .158 22. Net Effective Buying Inc. -.0 8 5 -.1 2 2 .415 .313 .066 .021 .223 23. Population Increase -.1 1 3 -.1 9 5 -.3 9 4 -.0 9 1 -.2 0 7 -.1 4 1 .145 24. Women Employed .147 .037 -.146 .092 -.313 .055 -.0 3 6 25. Geographical Location . 020 .198 .708 .255 .353 .194 .279 26. Crime Index -.0 6 0 -.0 8 6 .109 -.025 .085 -.071 -.0 8 1 27. Juvenile Delinquency -.0 5 5 -.0 2 4 -.2 7 1 .016 -.2 7 1 -.0 6 4 -.0 4 4 28. Public Welfare Expend. .306 .511 .182 .088 .013 .418 .111 29. Govt. Expend, on Health 1.000 .286 -.0 3 7 .110 -.0 7 9 .075 .003 30. Government Taxes 1.000 .230 .106 .009 .231 .104 31. Voting Behavior 1.000 .369 .211 .212 .291 32. Social Welfare Workers 1.000 .015 .026 .180 33. Payroll Deductions 1.000 .170 .166 34. Campaign V olunteers 1.000 .217 35. Campaign Budget 1.000 ^ = 252 for variables 26 to 31. N = 185 for variables 33 to 35 . N = 102 for v a ria b le 32. 144 TABLE 44. CORRELATION MATRIX OF ALL VARIABLES USED FOR CONSTRUCTING INSTRUMENTS BASED UPON 1960 DATA, COM MUNITY SIZE 25,000 AND OVER (N=100) V ariables 1 2 3 4 5 6 7 8 9 10 1. Campaign Achievement 1.000 .195 .057 .234 .052 .018 -.019 -.086 .007 -.1 2 6 2. Campaign Budget 1.000 .146 .299 .414 -.3 3 9 .338 -.1 5 2 .370 -.1 6 1 3. Payroll Deductions 1.000 -.0 3 3 .097 -.1 3 0 .211 -.2 4 4 -.0 5 0 -.1 1 1 4. Campaign V olunteers 1.000 .047 .014 .016 -.0 6 7 .035 -.1 2 2 5. Median Family Income 1.000 -.9 3 0 .648 .026 .544 .023 6. Poverty Index 1.000 -.691 .010 -.487 .025 7. Geographical Location 1.000 -.2 4 5 .438 -.0 9 1 8. In-migration 1.000 .107 .610 9. High School Enrollment 1.000 -.0 2 8 10. Population Increase 11. Women Employed 12. Unemployment 13. White Collar Workers 14. Median School Years Completed 15. Well-to-do Families 16. Population Size 17. Industrial Index 18. Dwelling Conditions 19. Net Effective Buying Income 20. Per Capita Giving TABLE 44 (CONTD.) Variables 11 12 13 14 15 16 17 18 19 20 1 . Campaign Achievement -.0 8 9 -.1 3 6 .022 .023 . 106 .169 .039 -.010 .043 .323 2. Campaign Budget .085 -.1 2 3 .094 .185 .368 -.116 .197 .227 .331 .751 3. Payroll Deductions -.1 7 9 .081 -.2 2 9 -.1 8 2 -.011 .167 .041 .118 .138 .252 4. Campaign V olunteers .135 -.004 .038 .017 .154 .144 -.028 .032 .038 .362 5. Median Family Income .095 -.2 0 1 .322 .509 .669 .256 .306 .703 .800 .513 6. Poverty Index -.0 4 8 .203 -.1 6 1 -.4 0 6 -.560 -.154 -.260 -.730 -.750 -.424 7. Geographical Location -.218 .212 -.155 .053 .412 .113 -.0 7 2 .587 .522 .264 8. In-migration .272 -.251 .609 .561 .079 .015 -.1 2 5 .088 -.047 -.233 9. High School Enrollment -.152 .039 .320 .538 .359 -.0 1 0 .167 .447 .366 .327 10. Population Increase .035 -.112 .227 .277 .054 .095 -.1 1 5 .180 .044 -.184 11. Women Employed 1.000 -.353 .338 .153 .154 -.0 4 5 .189 -.034 .193 .068 12. Unemployment 1.000 -.3 1 2 -.4 1 6 -.1 8 2 .045 -.2 3 9 -.1 5 0 -.1 3 5 -.1 8 1 13. White Collar Workers 1.000 .778 .276 .148 .239 .163 .280 .189 14. Median School Years Completed 1.000 .369 .077 .214 .409 .386 .246 15. Well-to-do Families 1.000 .233 .152 .464 .705 .435 16. Population Size 1.000 .195 .181 .312 .168 17. Industrial Index 1.000 .083 .348 .392 18. Dwelling Conditions 1.000 .598 .334 19. Net Effective Buying Income 1.000 .513 20. Per Capita Giving 1.000 It is important to mention that in the process of developing prediction instruments the aim is to find as many variables as possible that might be "independent" of each other, and yet significantly cor related with the criterion to be predicted. In the case of the multiple linear regression, hew ever, it is quite possible that even though a variable is insignificantly cor related with the criterion, it may still be selected as a predictor. For example, it will be seen from Table 16 that health index was included as one of the predictors for the size category, 50,000- 99, 999, although it is clear from Table 41 that the variable is very insignificantly correlated (.014) with the criterion. Hypothesis II. Reliable and Valid instruments for pre dicting community performance in term s of per capita giving to community fund-raising federations can be constructed on the basis of the configurational, multiple linear regression and other approaches. As already described in the preceding chapter, twenty-one instruments were built from data for 1950 and I960 according to four major prediction methods--configurational, multiple linear regression, Burgess and Glueck. All these instruments have been presented in that chapter. The problem now was to test the accuracy and efficiency of these instruments. This was done by using the instruments devel 147 oped from 1950 data to predict the outcome for a sample of 1951 comprising 200 communities and for two subsequent samples of 196liincluding 100 and 50 communities. Similarly, the instru ments constructed on I960 data were validated by using them to predict the outcome of communities included in the two samples of 1961. The whole process was carried out not only for the total samples and size categories, but also after excluding "Borderline communities." The "Borderline communities" were those falling between the 40th and 60th percentile with respect to per capita giving. The outcome predicted for the 1951 sample from the two types of predictive configuration instruments--contiguous cate gories and widespread categories, are presented according to community size in Tables 45 and 46. A comparison of these tables would reveal that the widespread categories instrument assigned lesser number of communities (21) to unpredictable category as compared to the contiguous type instrument (28). Also the number of communities predicted correctly was higher in case of the former (150) than in the case of the latter (143). A different pattern, however, emerged when these instru ments were used to predict the outcome for 1961 Sample I. It will be seen from the Tables 47 and 48 that the contiguous categories 148 TABLE 45* OUTCOME PREDICTED FOR 1951 SAMPIE FROM PREDICTIVE CONFIGURATION INSTRUMENT BASED UPON 195® DATA AND ACTUAL GIVING OUTCOME, BY SIZE OF COMMUNITY, CONTIGUOUS CATEGORIES, INSTRUMENT, DICHOTOMOUS CRITERION (N=200) Actual Giving ______Predicted Outcome Outcome and Size Borderline Commun- of Community ltles Excludeda Total Sample Unpre- Unpre High Low d ie te d T o ta l High Low dicted Total 25,000 and over High 75 8 10 93 89 15 14 118 Low 4 46 10 60 14 54 14 82 T o ta l 79 54 20 153 103 69 28 200 25,000 - 49,999 H igh 20 2 2 24 25 4 3 32 Low 1 12 3 16 5 15 4 24 T o ta l 21 14 5 40 30 19 7 56 50 ,0 0 0 - 9 9 ,9 9 9 High 21 4 3 28 26 6 4 36 Low 1 10 4 15 4 13 7 24 T o ta l 22 14 7 43 30 19 11 60 100,000 and over H igh 34 2 5 41 38 5 7 50 Low 2 24 3 29 5 26 3 34 T o ta l 36 26 8 70 43 31 10 84 ^Borderline communities were those falling between the 40th and 60th percentile according to per capita giving. 149 TABLE 4 6 . OUTCOME PREDICTED FOR 1951 SAMP IE FROM PREDICTIVE CONFIGURATION INSTRUMENT BASED UPON 1950 DATA AND ACTUAL GIVING OUTOME BY SIZE OF COMMUNITY* WIDESPREAD CATEGORIES INSTRUMENT, DICHOTOMOUS CRITERION (N=200) Actual Giving Predicted Outcome Outcome and B o r d e r lin e Commun- S iz e o f lties Excluded5 _ Total Sample Community Unpre- U npre- High Low dieted Total High Low dieted Total 2 5 ,0 0 0 and o v er High 83 3 7 93 100 8 10 118 Low 10 43 7 60 21 50 11 82 T o ta l 93 46 14 153 121 58 21 200 2 5 ,0 0 0 - 4 9 ,9 9 9 High 23 0 1 24 30 1 1 32 Low 3 12 1 16 7 16 1 24 T o ta l 26 12 2 40 37 17 2 56 5 0 ,0 0 0 - 9 9 ,9 9 9 High 25 2 1 28 32 2 2 36 Low 3 10 2 15 8 11 5 24 T o ta l 28 12 3 43 4 ° 13 7 60 1 0 0 ,0 0 0 and ov er High 35 1 5 41 38 5 7 50 Low 4 21 4 29 6 23 5 34 T o ta l 39 22 9 70 44 28 12 84 aBorderline communities were those falling between the 40th and 60th percentile according to per capital giving. 150 TABLE 4 7 . OUTCOME PREDICTED FOR 1961 SAMPLE ISIOM PREDICTIVE CONFIGURATION INSTRUMENT BASED UPON 1950 DATA AND ACTUAL GIVING OUTCOME, BY SIZE OF COMMUNITY: CONTIGUOUS CATE GORIES INSTRUMENT, DICHOTOMOUS CRITERION (N -100) Actual Giving Predicted Outcome Outcome and B o r d e r lin e Coraraun- S iz e o f ities Excluded^ Total Sample Community U npre- Unpre- 25,000 and over High 30 6 4 40 36 10 4 50 Low 7 30 3 40 9 36 5 50 T o ta l 37 36 7 80 45 46 9 100 25,000 - 49,999 High 4 2 1 7 7 3 1 11 Low 2 5 - 7 2 6 1 9 T o ta l 6 7 1 14 9 9 2 20 50 ,0 0 0 - 9 9 ,9 9 9 High 9 - 1 10 10 - 1 11 Low 3 1 0 2 15 3 13 3 19 T o ta l 12 10 3 25 13 13 4 30 100,000 and over High 17 4 2 23 19 7 2 28 Low 2 15 1 18 4 17 1 22 T o ta l 19 19 3 41 23 24 3 50 aBorderline communities were those falling between the 40 th and 60th percentile according to per capita giving. 151 TABLE 4 8 . OUTCOME PREDICTED FOR 1961 SAMPLE IUROM PEDICTIVE CONFIGURATION INSTRUMENT BASED UPON 1950 DATA AND ACTUAL GIVING OUTCOME, BY SIZE OF COMMUNITY: WIDE SPREAD CATEGORIES INSTRUMENTS, CICHOTOMOUS CRITERION (N=100) Actual Giving ______Predicted Outcome______Outcome and Borderline Commun- Size of ities Excluded8- Total Sample Community Uripre------Unpre^------______High Low dieted Total High Low dieted Total 25>000 and over H igh 26 10 4 40 32 13 5 50 Low 6 31 3 40 7 38 5 50 T o ta l 32 41 7 80 39 51 10 100 25 ,0 0 0 - 4 9 ,9 9 9 H igh 3 3 1 7 6 4 1 11 Low 2 5 - 7 3 5 1 9 T o ta l 5 8 1 14 9 9 2 20 50,000 - 9 9 ,9 9 9 H igh 9 1 10 9 2 11 Low 2 12 1 15 2 15 2 19 T o ta l 11 13 1 25 11 17 2 30 1 0 0 ,0 0 0 and over H igh 14 6 3 23 17 7 4 28 Low 2 14 2 18 2 18 2 22 T o ta l 16 20 5 41 19 25 6 50 aBorderline communities were those falling between the 40 th and 60th percentile according to per capita giving. 152 TABLE 4 9 . OUTCOME PREDICTED FOR 1961 SAMPLE I FROM PREDIC TIVE CONFIGURATION INSTRUMENT BASED UPON i 960 DATA AND ACTUAL GIVING OUTCOME, BY SIZE OF COMMUNITY: CONTIGU OUS CATEGORIES INSTRUMENT, DICHOTOMOUS CRITERION (N=100) Actual Giving Predicted Outcome______Outcome and Borderline Commun- Size of Community Ities Excludeda______Total Sample ~~ Gnpre- tlnpre- ______High Low dieted Total High Low dieted Total 2 5 ,0 0 0 and ov er H igh 31 5 4 40 34 10 16 50 Low 2 33 3 40 3 42 5 50 T o ta l 33 38 9 80 37 52 11 100 2 5 ,0 0 0 - 4 9 ,0 0 0 H igh 6 1 - 7 8 3 - 11 Low - 6 1 7 1 7 1 9 T o ta l 6 7 1 14 9 10 1 20 50,000 - 99,999 H igh 9 - 1 10 9 - 2 11 Low 1 10 4 15 2 13 4 19 T o ta l 10 10 5 25 11 13 6 30 1 0 0 ,0 0 0 and o v er High 16 4 3 23 17 7 4 28 Low 1 17 - 18 - 22 - 22 T o ta l 17 21 3 41 17 29 4 50 aBorder3kie communities were those falling between the 4 9 th and 60 th percentile according to per capita giving. 153 TABLE 5 0 . OUTCOME PREDICTED FOR 1961 SAMPLE I I FROM PREDIC TIVE CONFIGURATION INSTRUMENTS BASED UPON 1950 AND I960 DATA AND ACTUAL GIVING OUTCOME, BY SIZE OF COMMUNITY: CONTIGUOUS CATEGORIES INSTRUMENT: DICHOTO- MOUS CRITERION (N=50) Actual Giving Predicted Outcome for Total Sample Outcome and ; frPm Instrument Based upon Data S iz e o f f o r Community 1950 1365 Unpre Unpre- High Low d ic te d T o ta l High Low d ie te d T o ta l 25>000 and over H igh 21 3 3 27 19 4 4 27 Low 14 5 23 4 15 4 23 T o ta l 25 17 8 50 23 19 8 50 25,000 - 99,999 High 7 2 2 11 8 1 2 11 Low 3 8 3 14 4 7 3 14 T o ta l 10 10 5 25 12 8 5 25 100,000 and over High 14 1 1 16 11 3 2 16 Low 1 6 2 9 - 8 1 9 T o ta l 15 7 3 25 11 11 3 25 154 type assigned lesser number (2 8 ) to unpredictable and wrong cate gories as compared to the other type (3 0 ). The outcome predicted for the two 1961 validation samples for predictive configuration instrument based upon i9 6 0 data are presented in Tables and 50. Tables 51 to contain the prediction results for the three validation samples, one of 1951 and two of 1961, derived from the four selected instruments for each population category constructed according to the multiple linear regression method. Similarly the outcome predicted from the Burgess instruments based on 1950 data for the two validation samples (1951 sample and 1961 Sample I) are given in Tables 55 and 56; those for the Glueck instruments are presented in Tables 57 and 58* In Tables 59 and 60, the prediction results for 1961 Samples I and II from Glueck instru ment based upon i9 6 0 data are shown. The prediction results from the multiple regression and Glueck instruments for the 50 communities included in the 1961 Sample II are shown in Table 6l by the name and size of community. It will be seen from the table that the predicted amounts from the multiple linear regression instruments are, in most cases, within a reasonable degree of accuracy. In fact for a major part of the sample the predicted and actual amounts do not differ by more than 8 per cent for the i960 instrument, and 15 per cent for that of the 1950* 155 TABLE 5 1 . OUTCOME PREDICTED FOR 1951 SAMPLE FROM SELECTED LINEAR REGRESSION EQUATIONS BASED UPON 1950 DATA AND ACTUAL GIVING OUTCOME, BY SIZE OF COMMUNITY: DICHOTOMOUS CRITERION (N=200) Actual Giving Predicted Outcome Outcome and B o r d e r lin e Commun S iz e o f T o ta l Community ities Excluded3- Sam ple H igh Low T o ta l H igh Low T o ta l 2 5 ,0 0 0 and o v e r H igh 86 7 93 104 14 118 Low 5 55 60 12 70 82 T o ta l - 91 62 153 116 84 200 25,000 - 49,999 H igh 23 1 24 30 2 32 Low 1 15 16 3 21 24 T o ta l 24 16 40 33 23 56 50,000 - 9 9 ,9 9 9 H igh 27 1 28 33 3 36 Low 2 13 15 4 20 24 T o ta l 29 14 43 37 23 60 1 0 0 ,0 0 0 an d o v e r H igh 37 4 41 45 5 50 Low 3 26 29 4 30 34 T o ta l 40 30 70 49 35 84 aBorderline communities were those falling between the 40th and 60th percentile according to per capita giving. 156 TABLE 5 2 . OUTCOME PREDICTED FOR 1961 SAMPLEIJROM SELECTED LINEAR REGRESSION EQUATIONS BASED UPON 1950 DATA AND ACTUAL GIVING OUTCOME. BY SIZE OF COMMUNITY: DICHOT- MOUS CRITERION (N=100) P r e d ic te d Outcome Actual Giving Outcome and B o r d e r lin e Commun S iz e o f ities Excludeda Total Sample Community High Low T o ta l High Low T o ta l 2 5 j000 and over High 37 3 40 42 8 50 Low 8 32 40 15 35 50 T o ta l 45 35 80 57 43 100 25,000 - 49,999 High 6 1 7 9 2 11 Low 2 5 7 2 7 9 T o ta l 8 6 14 11 9 20 50,000 - 99,999 High 10 - 10 11 - 11 Low 1 14 15 5 14 19 T o ta l 11 14 25 16 14 30 100,000 and over High 20 3 23 24 4 28 Low 2 16 18 ■ 4 18 22 T o ta l 22 19 41 28 22 50 aBorderline communities were those falling between the 40th and 60th percentile according to per capita giving. 157 TABLE 53- OUTCOME PREDICTED FOR 1961 SAMPLE I FROM LINEAR REGRESSION EQUATIONS BASED UPON I960 DATA AND ACTUAL GIVING OUTCOME, BY SIZE OF COMMUNITY: DICHOTOMOUS CRITERION (N=100) Actual Giving Predicted Outcome Outcome and B o r d e r lin e Commun S iz e o f ities Excludeda Total Sample Community High Low Total High Low T o ta l 25,000 and over H igh 38 2 40 45 5 50 Low 8 32 40 13 37 50 T o ta l 46 34 80 38 42 100 25,000 - 49,999 H igh 7 - 7 10 1 11 Low 1 6 7 2 7 9 T o ta l 8 6 14 12 8 20 50,000 - 9 9 ,9 9 9 High 9 1 10 9 2 11 Low 1 14 15 2 17 19 T o ta l 10 15 25 11 19 30 100,000 and over High 23 - 23 28 - 28 Low — 18 18 2 20 22 T o ta l 23 18 41 30 20 50 aBorderline communities were those falling between the 40th and 60th percentile according to per capita giving. 158 TABLE 5^ . OUTCOME PREDICTED FOR 1961 SAMPLE I I FROM LINEAR REGRESSION EQUATIONS BASED UPON 1950 and i 960 DATA AND ACTUAL GIVING OUTCOME, BY SIZE OF COMMUNITY; DICHOTOMOUS CRITERION (N»50) Actual Giving Predicted Outcome for Total Sample Outcome and from Instrument Based upon Data S iz e o f ______f o r ______Community 1950 I960 High Low Total High Low Total 2 5 ,0 0 0 and o v er High 21 6 27 24 3 27 Low 7 16 23 6 17 23 T o ta l 28 22 50 30 20 50 25,000 - 99,999 High 9 2 11 9 2 11 Low 4 10 14 3 11 14 T o ta l 13 12 25 12 13 25 100,000 and over High ■13 3 16 16 - 16 Low 3 6 9 3 6 9 T o ta l 16 9 25 19 6 25 159 TABLE 5 5 . OUTCOME PREDICTED FOR 1951 SAMPLE FROM BURGESS INSTRUMENT BASED UPON 1950 DATA AND ACTUAL GIVING OUTCOME FOR 1951 SAMPLE, BY SIZE OF COMMUNITY: DICHOTOMOUS CRITERION (N=200) Actual Giving Prediction Outcome Outcome and Borderline Commun Size of ities Excluded® Total Sample Community High Low Total High Low Total 2 5 ,0 0 0 and over High 81 12 93 92 26 118 Low 16 44 60 27 55 82 Total 97 56 153 119 81 200 25,000 - 49,999 High 22 2 24 24 8 32 Low 6 10 16 14 10 24 Total 28 12 40 38 18 56 5 0 ,0 0 0 - 9 9 ,9 9 9 High 23 5 28 27 9 36 Low 2 13 15 3 21 24 Total 25 18 43 30 30 60 1 0 0 ,0 0 0 and over High 36 5 41 41 9 50 Low 8 21 29 10 24 34 Total 44 26 70 51 33 84 aBorderline communities were those falling between the 40 th and 60th percentile according to per capita giving. 160 TABLE 5 6 . OUTCOME PREDICTED FOR 1961 SAMPLE IEBOM BURGESS INSTRUMENT BASED UPON 1950 DATA AND ACTUAL GIVING OUTCOME, BY SIZE OF COMMUNITY: DICHOTOMOUS CRITERION (N=100) Actual Giving Predicted Outcome Outcome and B o r d e r lin e Commun S iz e o f ities Excludeda Total Sample Community High Low T o ta l High Low T o ta l 25,000 and over High 36 4 40 43 7 50 Low 13 27 40 21 29 50 T o ta l 49 31 80 64 36 100 25,000 - 49,999 High 6 1 7 9 2 11 Low 3 4 7 5 4 9 T o ta l 9 5 14 14 6 20 5 0 ,0 0 0 - 9 9 ,9 9 9 H igh 10 10 11 11 Low 4 11 15 7 12 19 T o ta l 14 11 25 18 12 30 100,000 and over High 20 3 23 23 5 28 Low 6 12 18 9 13 22 T o ta l 26 15 41 32 18 50 0 Borderline communities were those falling between the 40th and 60th percentile according to per capita giving. 161 TABLE 5 7 . OUTCOME PREDICTED FOR 1951 SAMPLE FROM GLUECK INSTRUMENT BASED UPON 1950 DATA AND ACTUAL GIVING OUTCOME, BY SIZE OF COMMUNITY: DICHOTOMOUS CRITERION (N=200) Actual Giving Predicted Outcome Outcome and Borderline Commun Size of ities Excluded^ Total Sample Community High Low Total High Low Total 25,000 and over High 88 5 93 109 9 118 Low 13 47 60 24 58 82 Total 101 52 153 133 67 200 2 5,000 - 4 9 ,9 9 9 High 24 - 24 32 - 32 Low 5 11 16 9 15 24 Total 29 11 40 41 15 56 50,000 - 99,999 High 26 2 28 32 4 36 Low 3 12 15 8 17 24 Total 29 14 43 40 21 60 100,000 and over High 38 3 41 45 5 50 Low 5 24 29 8 26 34 Total 43 27 70 53 31 84 ^Borderline communities were those falling between the 4 0 th and 60th percentile according to per capita giving. 162 TABLE 5 8 . OUTCOME PREDICTED FOR 1961 SAMPLEIFROM GLUECK INSTRUMENT BASED UPON 1950 DATA AND ACTUAL GIVING OUTCOME, BY SIZE OF COMMUNITY: DICHOTOMOUS CRITERION (N=100) Actual Giving Predicted (Outcome Outcome and B o r d e r lin e Commun S iz e o f ities Excluded 3 Total Sample Community High Low T o ta l High Low Total 25,000 and over H igh 36 4 40 41 9 50 Low 6 34 40 12 38 50 T o ta l 42 38 80 53 47 100 25 ,0 0 0 - 49,999 High 6 1 7 8 3 11 Low 3 4 7 5 4 9 T o ta l 9 5 14 13 7 20 50 ,0 0 0 - 9 9 ,9 9 9 High 10 - 10 10 1 11 Low 3 12 15 5 14 19 T o ta l 13 12 25 15 15 30 100,000 and over High 20 3 23 23 5 28 Low - 18 18 2 20 22 T o ta l 20 21 41 25 25 50 aBorderline communities were those falling between the 40 th and 6 0 th percentile according to per capita giving. 163 TABLE 5 9 . OUTCOME PREDICTED FOR 1961 SAMPLE I FROM GLUECK INSTRUMENT BASED UPON i 960 DATA AND ACTUAL GIVING OUTCOME, BY SIZE OF COMMUNITY: DICHOTOMOUS CRITERION (N=100) Actual Giving Predicted Outcome Outcome and B o r d e r lin e Commun S iz e o f ities Excludeda T o ta l Sample Community High Low T o ta l High Low T o ta l 25*000 and over High 36 4 40 44 6 50 Low 3 37 40 7 43 50 T o ta l 39 41 80 51 49 100 2 5,000 - 4 9 ,9 9 9 High 6 1 7 10 1 11 Low 2 5 7 4 5 9 T o ta l 8 6 14 14 6 20 50,000 - 9 9 ,9 9 9 High 9 1 10 9 2 11 Low 1 14 15 2 17 19 T o ta l 10 13 25 11 19 30 100,000 and over High 21 2 23 25 3 28 Low — 18 18 1 21 22 T o ta l 21 20 41 26 24 50 a Borderline communities were those falling between the 40 th and 60th percentile according to per capita giving. 164 TABLE 60. OUTCOME PREDICTED FOR 1961 SAMPLE II FROM GLUECK INSTRUMENTS BASED UPON 1950 AND 1960 DATA AND ACTUAL GIVING OUTCOME, BY SIZE OF COMMUNITY: DICHOTOMOUS CRITERION (N=50) Actual Giving Outcome P redicted Outcome for T otal Sample .Instrument and Based Upon Data for - S iz e of Community 1950 1960 High Low T otal High Low T otal 25,000 and Over High 25 2 27 23 4 27 Low 7 16 23 3 20 23 TOTAL 32 18 50 26 24 50 25,000 - 99,999 High 10 1 11 8 3 11 Low 5 9 14 2 12 14 TOTAL 15 10 25 10 15 25 100,000 and Over High 15 1 16 15 1 16 Low 2 7 9 1 8 9 TOTAL 17 8 25 16 9 25 165 TABLE 61. OUTCOME PREDICTED FOR 1961 SAMPLE II FOR INSTRUMENTS CONSTRUCTED ACCORDING TO MULTIPLE LINEAR REGRESSION AND GLUECK METHODS, BY NAME AND SIZE OF COMMUNITY: DICHOTOMOUS CRITERION (N=50) Outcome P redicted by - Multiple Linear Regression Glueck Instruments Actual Instruments based on - based on - Community Population Giving 1950 Data 1960 Data 1950 Data 1960 Data ( in High High High High .... High Thousands) $ or Low3 $ or Low3 $ or Low3 Score*5 or Low3 Score'5 or Low3 Selma, Ala. 57 2.17 L 1.37 L 3.00 L 194 L 200 L G lendale, C a lif. 140 2.22 L 4.10 H 4.90 H 297 L 295 L Pomona,Calif. 83 2.65 L 2.95 L 2.70 L 297 L 350 H San Diego, C a lif. 1,033 2.77 L 4.44 H 4.43 H 226 L 275 L Denver, Colo. 885 4.53 H 3.28 L • 4.69 H 343 H 350 H New Britain, Conn. 92 4.63 H 4.45 H 4.04 H 379 H 385 H Wilmington, D el. 290 7.49 H 5.53 H 7.01 H 408 H 420 H A tlan ta, Ga. 927 3.71 L 3.78 L 4.19 H 347 H 315 H P ensacola, Fla. 174 3.03 L 2.45 L 3 .20 L 261 L 220 L Columbus, Ga. 205 2.79 L 2.00 L 3.12 L 149 L 150 L E lgin , 111. 49 4.41 H 4.52 H 6.72 H 425 H 425 H Kankakee, 111. 42 3.97 H 5.29 H 4.75 H 400 H 400 H Springfield, 111. 147 4.41 H 5.98 H 5.70 H 350 H 350 H Gary, Ind. 178 4.65 H 3.40 L 4.32 H 320 H 320 H Richmond, Ind. 75 4.03 H 3.20 L 3.83 H 307 L 260 L Cedar Rapids, Iowa 103 5.35 H 5.41 H 5.64 H 396 H 425 H Council Bluffe, Iowa 55 2.20 L 3.75 L 2.88 L 323 H 305 L Topeka, Kans. 119 4.66 H 3.70 L 4.15 H 295 L 245 L TABLE 61 (CONTD.) Outcome P redicted by - Multiple Linear Regression Glueck Instruments Actual Instruments based on - based on - Community Population Giving 1950 Data 1960Data 1950 Data 1960 :Data (in High High High High High Thousands) $ or Lowa $ or Lowa $ or Lowa Scored or Low3 Score*3 or Lowa New Orleans, La. 860 3.83 H 4.12 H 3.88 H 344 H 360 H Lansing, Mich. 220 5.98 H 5.20 H 6.40 H 392 H 375 H Joplin, Mo. 39 4.30 H 4.02 H 4.18 H 343 H 340 H S t. Louis, Mo. 1,507 5.71 H 4.54 H 4.67 H 393 H 390 H Springfield, Mo. 96 3.94 H 3.62 L 3.55 L 316 H 305 L Atlantic City, N. J. 80 2.20 L 2.41 L 3.11 L 295 L 295 L Amsterdam, N.Y. 29 3.25 L 4.92 H 3.98 H 325 H 325 H Binghamton, N. Y. 210 5.08 H 5.03 H 5.00 H 375 H 375 H Cortland, N. Y. 41 3.70 L 4.71 H 3.50 L 293 L 293 L Watertown, N. Y. 35 5.21 H 4.76 H 4.72 H 335 H 335 H White Plains, N. Y. 50 4.90 H 4.57 H 7.14 H 435 H 435 H A s h e v ille , N.C. 130 3.78 L 4.79 H 3.80 L 287 L 285 L Greensboro, N. C. 120 6.67 H 4.86 H 5.60 H 366 H 350 H Columbus, Ohio 696 5.06 H 4.39 H 4.82 H 337 H 340 H Loraine, Ohio 92 4.57 H 4.05 H 4.22 H 318 H 310 L Marion, Ohio 60 3.72 L 3.80 L 3.68 L 346 H 300 L Portsmouth, Ohio 84 2.03 L 3.20 L 2.37 L 229 L 205 L Youngstown, Ohio 226 5.14 H 4.63 H 4.73 H 383 H 315 H Lancaster, Pa. 278 3.74 L 4.12 H 3.80 L 356 H 265 L York, Pa. 236 3.33 L 3.75 L 3.19 L 279 L 225 H L L L L L H H H H H H H High or Low 175 380 330 215 335 395 230 320 385 410 330 230 based on - H L L L L H H L H H H High or Lowa Score*5 Data 1960 Data Glueck Instruments 1950 334 201 247 304 425 378 405 276 L H 398 H L L L H L L H 391 High or Low3 Score*5 $ Outcome P redicted by - 0.94 4.96 3.45 2.88 L 298 4.34 3.90 3.33 3.14 L L L H L L H L H 3.58 HH 4.72 5.37 H H 342 High or Lowa Data 1960 Data Instruments based on - Multiple Linear Regression $ 4.53 3.48 3.60 4.70 4.81 3.22 5.00 H 4.32 4.76 4.52 3.65 2.99 L L L H L L L . 3.69 H H L L H H High or Lowa Actual $ 3.28 3,98 1.67 5.00 5.04 3.46 2.78 90 85 68 3.70 89 ( in ( 186 3.43 935 408 235 4.27 Thousands) Population Giving50 19 S. Dak. Va. Sioux Falls, TABLE 61 (CONTD.) Community ^The ^The Median Value on Percapita Giving (3.82) was the cutting point between the "High" and "Low" Categories. Abilene, Austin, Tex. Tex. Richmond, Va. S e a tt le , Wash. Sheboygan, Wis 51 ^Score of 315 was the99, 999 cutting and 100,000 point or over between for the the "High" and Instrument "Low" Categories constructed for the on 1960 Instrument Data. constructed Tacoma, Wash. Lynchberg, Va. 55 5.65 Manitowac, Wis 32 3.40 on 1950 Data. Scores of 315,325 and 310 were the cutting points for Community Size 25,000-49,000, 50,GOO- Vancouver, Wash. 84 Wheeling, W. Wausau, Wis. 168 In Table 62, the results on predictive accuracy of the instru ments constructed according to the four Prediction methods are presented for the three validation samples--1951 (N=200), 1961 I (N=T00), 1961 II (N=50). Similar results for the three samples after excluding Borderline communities are presented in Table 63. From the tables 62 and 63 it will be seen that the predictive accuracy of the instruments for most size categories was 70 per cent or more. As a matter of fact, all the instruments had pre dictive accuracy of 76 per cent or higher for one or more of the categories. This was quite a reasonable degree of accuracy, es pecially in view of the fact that most of the instruments were con structed on data for 1950 and were used for predicting outcome for 1961 samples. During this span of ten years, considerable changes might have taken place in the social and economic charac teristics of the communities. Moreover a considerable portion of data obtained from the questionnaires, addressed to the respective fund-raising federations, particularly those for the year 1950 per taining to the number of volunteers and the payroll deductions were, for the most part, based on rough estimates. Another factor that might have adversely affected the accuracy of the instruments stemmed from the fact that the communities in cluded in each sample were highly heterogeneous. Most of the 169 TABLE 62. PREDICTIVE ACCURACY OF ALTERNATIVE PREDICTION METHODS, BY SIZE OF COMMUNITY: TOTAL SAMPLE: DICHOTOMOUS CRITERION Instruments V alidated on - P re d ic tio n Method 1951 Sample (N==200) 1961 Sample I (N=100) 1961 Sample II (N=50) Used For Instru 25,000 25,000 50,000 100,000 25,000 25,000 50,000 100,000 25,000 25,000 100,000 ments Constructed and to to and and to to and and to and on - over 49,999 99,999 over over 49,999 99,999 over over 99,999 over (N=200) (N=56) (N=60) (N=84) (N=100) (N=20) (N=30) (N=50) (N=50) (N=25) (N=25) 1950 Data Conf igur at ional Contiguous C ategories .72 .71 .65 .76 .72 .65 .77 .72 .70 .60 .80 Widespread Categories .75 .82 .72 .73 .70 .55 .80 .70 .71 .59 .78 Multiple Regres sion .87 .91 .88 .89 .77 .80 .83 .84 .74 .76 .76 Burgess .74 .60 .80 .77 .72 .65 .77 .72 .71 .68 .74 Glueck .83 .84 .80 .85 .79 .68 .80 .86 .82 .76 .88 50 Data Configurational Contiguous Categories .76 .75 .73 .78 .68 .60 .76 Multiple Regres sion .82 .85 .87 .96 .82 .80 .88 Glueck .87.75 .87 .92 .86 .80 .92 TABLE 63. PREDICTIVE ACCURACY OF ALTERNATIVE PREDICTION METHODS, BY SIZE OF COMMUNITY; BORDERLINE COM MUNITIES EXCLUDED: DICHOTOMOUS CRITERION Instrum ents V alidated on - P red ictio n Method 1951 Sample (N==153) 1961 Sample I (N=80) 1961 Sample II (N=40) Used For Instru 25,000 25,000 50,000 100,000 25,000 25.000 50,000 100,000 25,000 25,000 100,000 ments Constructed and to to and and to to and and to and on - over 49,999 99,999 over over 49.000 99,999 over over 99,999 over (N=153) (N=40) (N=43) (N=70) (N=80) (N=14) (N=25) (N=41) (N=40) (N=19) (N=21) 1950 Data Configurational Contiguous C ategories .79 .80 .72 .83 .79 .78 .74 .83 .80 .74 .86 Widespread Categories .82 .87 .81 .80 .71 .57 .84 .68 .71 .59 .83 Multiple Regres- sion .92 .95 .93 .90 .86 .79 .96 .88 .74 .89 .81 Burgess .82 .80 .84 : .81 .79 .71 .84 .78 .75 .71 .79 G1 ueck .88 .89 .88 .88 .88 .71 .88 .93 .87 .79 .95 1960 Data Configurational Contiguous C ategories .80 .86 .76 .80 .79 .72 .86 Multiple Regres sio n .88 .93 .92 1.00 .88 .89 .95 Glueck .91 .79 .92 .95 .90 .83 .95 instruments were constructed for the total sample, that is, all the communities irrespective of size were included in the process of construction. Apart from the variation in population size there were other significant characterisitcs of communities that differed widely; for example, one can hardly expect any similarity between the Metropolitan communities like Philadelphia and Chicago and small urban communities such as El Dorado (25,000) in Arkansas and Yuma in Arizona. In the light of the above facts, it is quite valid to conclude that reliable and valid instruments for predicting community per formance in term s of per capita giving can be constructed on the basis of the four prediction methods. Hypothesis III--Instruments based on the multiple linear regression approach predict with greater accuracy and efficiency, and are more stable as compared to those based on the other three approaches. Relative accuracy, efficiency and stability were tested for the total validation samples as well as after excluding borderline communities. The coefficients of accuracy, efficiency and stability were computed, in accordance with the procedure described in the preceding chapter, for all the three validation samples. The results on predictive accuracy for the total samples and for the 172 TABLE 6 4 . PROPORTION OP COMMUNITIES IN EACH CRITERION CATEGORY PREDICTED CORRECTLY BY ALTERNATIVE PREDICTION METHODS: DICHOTOMOUS CRITERION Predition Method High Category Low C ategory Used for Instru 1951 l$ b l 1961 1951 1961 1961 ments Constructed Sample Sample Sample Sample Sample Sample on— I II I II 1950 Data P r e d ic tiv e Configuration C ontiguous C a te g o r ie s • 75 • 72 • 71 .6 6 .7 2 .52 W idespread C a te g o r ie s .85 .64 .73 .61 .76 .62 Multiple Linear R e g r e ssio n .9 2 .8 8 .8 1 .87 .7 8 • 72 Burgess Unit- W eighting • 78 .8 6 .8 0 .67 • 58 .62 G lueck • 92 .82 .96 • 70 .7 6 • 74 I9 6 0 Data P r e d ic tiv e Configuration C ontiguous C a te g o r ie s .6 8 • 74 .8 4 • 70 Multiple Linear R e g re ssio n • 94 • 93 .8 8 .74 G lueck .88 .8 5 .8 6 .87 Source: Tables 45-6®• 173 TABLE 6 5 . PREDICTIVE EFFICIENCY OF ALTERNATIVE PREDICTION METHODS: DICHOTOMOUS CRITERION Prediction Method Borderline Commun- Used for Inatru- ities Excluded3- Total Sample______ments Constructed 1951 19bl l9bl 1951 19bl 1961 on— Sample Sample Sample Sample Sample Sample I I I I I I 1950 Data P r e d ic tiv e Configuration C ontiguous C a te g o r ie s •47b .50 .61 •30b .4 4 .3 9 W idespread C a te g o r ie s • 55b .43 .46 •39b .^0 .35 Multiple Linear R e g r e ssio n •8ob .78 .66 •74b .66 .50 Burgess Unit- W eighting • 53b .58 .4 8 •35 *44 .32 G luek •70b .75 .78 . 60b .58 .70 i 960 Data P r e d ic tiv e Configuration C ontiguous C a te g o r ie s . 60b .56 *52b .35 Multiple Linear R e g r e ssio n •93b *78 . 82 b .65 Glueck .83b .7 8 .7 4 b .7 0 Source: Tables 45-60. aBorderline communities were those falling between the 40th and 60th percentile according to per capita giving. bInitial criterion sample. 174 TABLE 66. LEVEL OF SIGNIFICANCE OF CRITICAL RATIOS REPRESENTING DIFFERENCES IN EFFICIENCY BETWEEN ALTERNATIVE PREDICTION METHODS (M. L. R. denotes Multiple Linear Regression; P. C. denotes Predictive Conf igur ation.) Level of Significance Borderline Communities Total Prediction Methods and Excluded3 Sample Data Used 1951 1961 1961 1951 1961 1961 Sample Sample Sample Sample Sample Sample I II I II 1950 M. L. R. - Glueck .05 N.S N.S .01 N.S .05 M. L. R. - P. C. (Contiguous) .01 .01 N.S .01 .01 N.S M. L. R. - P. C. (Widespread) .01 .01 .10 .01 .01 N.S M. L. R. - Burgess .01 .01 .10 .01 .01 .10 Glueck - P. C. (Contiguous) .02 .01 .10 .01 .05 .01 Glueck - p. C, (Widespread) .01 .01 .01 .01 .02 .01 Glueck - Burgess .01 .05 .01 .01 .05 .01 P. C. (Contiguous) - P. C. (Widespread) N.S N.S N.S .10 N.S N.S P. C. (C ontiguous)- Burgess N.SN.S N.S N.SN.SN.S P. C. (Widespread) - Burgess N.S .10 N.SN.S N.SN.S 1960 M. L. R. - Glueck .05 N.S N.S N.S M. L. R. - P. C. (Contiguous) .01 .05 .01 .01 Glueck - P. C. (Contiguous) .01 .05 .01 .01 aBorderline Communities were those falling between the 40th and 60th per centile according to percapita giving. 175 TABLE 6 7 . RELATIVE PREDICTIVE STABILITY CP ALTERNATIVE PREDICTION METHODS: DICHOTOMOUS CRITERION Prediction Method Borderline Commun Used for Instru ities Excluded8- Total Sample ments Constructed 1951 1961 1961 1951 1961 1961 on— Sample Sample Sample Sample Sample Sample I II I II 1950 Data Predictive Configuration Contiguous Categories 1 .0 0 b 1 .0 6 1 .3 0 1 .0 0 b 1 .4 7 1 .3 0 Widespread Categories 1 .0 0 b O.78 O.83 1 .0 0 b I .03 O.89 Multiple Linear Regression 1 .0 0 b O.98 0 .8 2 1 .0 0 b 0 .8 9 0 .7 0 Burgess Unit- Weighting 1 .0 0 b 1 .0 9 0 .9 0 1 .0 0 b 1 .2 6 0 .9 1 Glueck 1 .0 0 b 1 .0 7 1 .1 1 1 .0 0 b 0 .9 7 1 .1 7 I9 6 0 Data Predictive Configuration \ Contiguous Categories 1 .0 0 b • 93 1 .0 0 b • 70 Multiple Linear Regression 1 .0 0 b .80 1 .0 0 b .8 0 Glueck 1 .0 0 b .9 4 1 .0 0 b .94 Source: Table 6 5 . aBorderline communities were those falling between the 40th and 60th percentile according to per capita giving. ^Initial criterion sample. 176 remaining samples after excluding the borderline communities have already been presented in Tables 62 and 63 respectively. In order to test the discriminative ability of the instruments, their predictive accuracy for each criterion category "high" and "low" were determined separately, that is, the proportion of communities in each criterion category predicted correctly by alternative prediction methods was computed (see Table 64). Tables 65 and 67 contain the results on predictive efficiency and stability respectively. To determine whether the differences in the coefficients of efficiency among the instruments were statis tically significant the use of the critical ratio was made. The level of significance of the computed values are given in Table 66. 1. Predictive accuracy and efficiency. It will be seen from Tables 62, 63 and 65 that, for the most part, the instruments con structed according to multiple linear regression method gave the best results both for the total samples and for the different size categories. The range of their accuracy for the total samples varied from 7^ to 96 per cent for the dichotomous criterion. The instruments constructed according to the Glueck method were found to be the next most accurate. This was generally true both for the instruments constructed on 1950 data as well as for those based on the data for I960, thought a different trend emerged when they 177 were used on the 1961 Sample II. It was found that for 1961 Sample II Glueck method, on the whole, gave superior results in term s of accuracy and efficiency as compared to all other methods, includ ing the multiple linear regression. Almost similar results as noted for the total samples were obtained when the borderline com munities were excluded. The instruments based on the Burgess and predictive configuration methods were found consistently to be less superior as compared to multiple linear regression and Glueck methods. The aforesaid observations concerning the relative accuracy and efficiency of the instruments are also borne out from Table 66 which presents the critical ratios representing the differences in efficiency between the alternative prediction methods. A closer examination of Tables 65 and 66 would reveal that among the in struments based on 1950 data and validated on the sample of 1951 the coefficient of efficiency (.74) for multiple linear regression instruments was significantly higher than those for all other instru ments, including the one based on the Glueck method (.60); however, when the same instruments were used on the 1961 Sample II, the A value for the Glueck instrument (. 70) was significantly higher as compared to all others, including the multiple linear regression instruments (.50). The difference in the values of the coefficient 178 of efficiency among the multiple linear .regression and Glueck instruments, constructed on the 1950 and I960 data and applied on the 1961 Sample I, were found not to be significantly different; although as compared to others, their values were significantly h ig h e r, When the borderline communities were excluded, it was found that among the instruments constructed on the 1950 data and applied on the 1951 validation sample, the multiple linear re gression instruments had significantly higher value on the index in comparison with all others; this was also true for the instru ment based on the I960 data and applied on the 1961 Sample I. Of those constructed on the 1950 data and validated on the 1961 Sample I and 1961 Sample II, the instruments based on multiple linear re gression and Glueck methods were again found to be significantly superior as compared to the others, although the differences in the coefficient of efficiency among them were not statistically signifi can t. It will be seen from Table 65 that the coefficients of efficiency of instruments constructed according to the multiple linear regres sion^ method ranged from 5 0 to 82 per cent for the total samples, and from 66 to 93 per cent on the exclusion of borderline communi ties, For the Glueck instruments the corresponding ranges were 179 from 58 to 74 per cent, and from 70 to 83 per cent respectively. It may be noted that in the Stuckert's study the range of the coeffi- cienteof efficiency of instruments, used to predict the outcome of 1 total sample* was from 17. 9 to 55,0. 2. Discriminative ability. It is evident from Table 64 that instruments based on the multiple linear regression method pre dicted correctly greater proportion of communities in each criterion category; the Glueck instruments were again the second best. It is interesting to note that the Burgess instrument based on 1950 data was the second best in predicting the "high" category of the 1961 Sample I; it had the lowest accuracy, however, in pre dicting the outcome for the "low" category. Most of the instru ments predicted the "high" category with greater accuracy as compared to the corresponding "low" category. The instruments based on the predictive configuration method, however, generally predicted the outcome for the "low" category with greater or equal accuracy as compared to the "high" category. 3. Predictive stability. As already stated, stability refers to the ability of a prediction instrument to predict the criterion outcome for subsequent samples as efficiently as for the initial sample used in the construction of the instrument. It will be seen ^Stuckert, op. cit. , p. 64. 180 from Table 67 that almost all the instruments had high indices on stability. In no case did the value on the index drop below 70 per cen t. In the Stuckert’s study the coefficient of stability for one of the instruments, when used on a total sample, had dropped to as low as 48 per cent even though the interval between the initial criterion sample and the subsequent one did not exceed one year; the maximum value for any instrument, save those based on the 2 multiple linear regression method, was not higher than 99 per cent. It is important to note that one or more of the instruments constructed according to the predictive configuration, Burgess and Glueck methods in some cases had the coefficient of stability exceeding 1.00. In other words, they were predicting the outcome for 1961 sample with greater efficiency than for the original sample of 1951. The reason why the coefficients of stability for the multiple linear regression instruments were relatively lower as compared to some of the instruments based on other methods might be at tributed to the fact that in contrast to the other methods, the form er, by and large, had higher coefficients of efficiency for the initial 2 Ibid., p. 70. 181 criterion sample, and hence there was a lesser likelihood of obtain ing by chance a higher value on the index for the subsequent sample. On the basis of the above analysis it may be concluded that the instruments constructed according to multiple linear regression method predicted the giving to a community fund-raising federa tion with greater accuracy and efficiency as compared to the instru ments based on the other three methods. Also, the multiple linear regression instruments had greater ability to discriminate between the dichotomous criterion categories. Their stability was also reasonably high, though it was not as high as for some of the in struments based on the other methods. Thus it may be observed that the Hypothesis III which stated that instruments constructed on the multiple linear regression approach have greater predictive accuracy, efficiency and stability is supported in general, except for the part pertaining to stability. 182 CH A PTER V SUMMARY AND CONCLUSIONS Scientific social prediction has hardly more than forty years of history. During this period there has been phenomenal growth in the number of studies involving prediction of a social phenomenon. These studies have encompassed a wide variety of spheres includ ing crime, delinquency, parole, probation, marriage, rehabilita tion, education, voting, leadership, and hospitalization. Broadly speaking, there have been developed three major approaches for social prediction, namely, factor additive, multiple linear^ regression, and prediction-by-classification. The factor additive approach has been used most frequently in predicting parole or m arital success; the two variations of this method are the Burgess unit-weighting and Glueck techniques. Predictive configuration is an improved variety of the prediction by classifica tion approach. The various social prediction methods developed so far have been, prim arily, used to predict some aspect of a phenomenon concerning individuals. Review of the literature reveals that 183 hardly any attempts have been made to predict a community varia b le . The primary problem in this research was to explore the potentialities of predicting a community phenomenon. It was felt that the methods already developed and used for prediction of individual behavior or performance might be employed, with some modifications, to predict a community variable. Summary of Research Twenty-one instruments to predict per capita giving to fund-raising federations were constructed according to four methods--multiple linear regression, Glueck, configuration, and Burgess. Of these twenty-one, sixteen were based on 1950 data and the remaining five on those for I960. For constructing instruments using 1950 data, 350 communi ties were selected from the 1951 Directory of the United Community Funds and Councils of America, Incorporated. All communities in the continental United States of America whose population, according to the United States Bureau of the Census, exceeded 25,000 in 1950 were included, provided they met some other cri teria that were necessary for collecting data on a uniform basis. Thus those communities whose population figures as given in the 1950 census reports differed by more than 15 per cent from those 184 specified in the 1951 Directory of the United Community Funds and Councils of America, Incorporated, were excluded, since it was felt that the data obtained on a community from the census and other reports would not correctly represent that included in the cam p aig n . Similarly, to construct instruments based on I960 data another sample of one hundred communities was selected from the 1961 Directory of the aforesaid organization, by utilizing the method of disproportionate sampling; this was done to insure the inclusion of an adequate number of communities representing the different-sized categories and geographical areas. Almost all these communities had been included in the earlier sample of 1951. For the validation process three sam ples--1951 Sample of 200, 1961 Sample I of 100 and 1961 Sample II of 50, were used. The first of these included all the communities among the 350 for whom complete responses to the questionnaire had been received from the respective fund-raising federations. The second, that , is, 1961 Sample I, was the same that was used in the construction of instruments based on I960 data. The third sample, including 50 communities, was selected from the 1951 validation sample after excluding communities included in the 1961 Sample I. The data used in the construction of the instruments were ob tained prim arily from publications of the United States Bureau of the Census and United Community Funds and Councils of America, Incorporated. Some data were also obtained from reports of other government or voluntary organizations. In addition, some infor mation was obtained by means of a questionnaire addressed to the executives of the respective fund-raising federations through the Research department of the United Community Funds and Councils of America, Incorporated. All the instruments except those based on the multiple linear regression method were constructed according to the dichotomous criterion, that is, to predict the giving in term s of "high" and "low. " The medians of the per capita giving for the two samples of 1951 and 1961, used in the construction, were employed as the criteria for the "high" and "low" for the samples of the respective years. The instruments based on multiple linear regression pre dicted the giving in actual amounts (see Table 61). Predictions resulting from the use of the instruments con structed according to the four methods were compared by using them to predict the outcome for the validation samples. To enable objective comparison among them coefficients of accuracy, effi ciency, and stability were computed. 186 Almost all the instruments were found to predict with a reasonable degree of accuracy and efficiency. The instruments based on the..'..method of multiple linear regression were, by and large, superior to the others in term s of accuracy and efficiency. The accuracy of one of them on the basis of the dichotomous cri terion for one of the population categories of a total sample was as high as 96 per cent; when the borderline communities were excluded from the sample, it yielded 100 per cent correct results. Glueck-type instruments were the next best. As a matter of fact, in case of one of the validation samples, the Glueck instruments gave the best results. The stability of the instruments was also fairly high, even though in case of most instruments the interval involved was ten years. The stability refers to the ability of an instrument to pre dict the outcome for subsequent samples after a lapse of time with the same degree of accuracy as for the original sample used in the construction. The instruments based on the multiple linear regression method were not the most stable ones, though their stability was quite high. Interestingly, some of the instruments although built on 1950 data predicted the outcome for 1961 sample with greater accuracy than for the original sample of 1951. The instruments were also tested for their ability to predict the outcome for communities falling in each category of the cri terion. It was found that, for the most part, the instruments pre dicted the outcome for both categories equally well, although some instruments did predict the outcome for the "high" category with somewhat greater accuracy than for the other. It is important to compare these instruments with others developed for predicting some criterion in case of individuals. In reality very few prediction instruments, developed in social sciences, have been adequately tested for validity, in terms of accuracy, efficiency, and stability. In relation to those that have been tested, it may be observed that some of the instruments developed in this study, particularly the multiple linear regression and Glueck type instruments, yielded better or at least equally good re s u its . Some discussion regarding the question as to which variables were found to be most significantly related to giving is of consid erable interest to community organization workers, particularly those engaged in fund-raising. A number of variables mainly of economic nature were noted to be significantly correlated with giving. Some of these were campaign budget (per capita expendi ture on campaign and administration), campaign achievement 188 (ratio of the amount raised to the goal during the preceding year), median family income, poverty index, number of campaign volunteers per 10, 000 population, proportion of non-firm gifts raised through payroll deduction, industrial index, net effective buying income, geographical location and in-migration. Multiple linear regressions yielded some interesting results concerning the relationship of individual variables with the giving. It was noted that when the interacting or intruding effect of all the variables used in the study except the particular one studied was eliminated or kept constant, giving was generally higher in the presence of the following factors: 1. Higher per capita expenditure on campaign and administration; 2. Larger number of campaign volunteers per 10,000 pop u latio n ; 3. Higher proportion of non-firm gifts raised by payroll deduction; 4. Higher campaign achievement; 5. Lower in-migration; 6. Lower percentage of persons, 25 years old and over, having high school or more education; 7. Lower population increase; 189 8. Greater poverty index (greater percentage of families having annual income below $2,000 in 1950 or $3,000 in 1960); 9. Higher infant mortality; 10. Higher percentage of unskilled workers; 11. Higher percentage of white collar workers; 12. Higher net effective buying income; 13. Higher number of social welfare workers per 10,000 population; 14. Higher industrial index (sum of per capita value added by manu facture, per capita wholesale sales, per capita retail sales, and per capita receipts from personal, business and repair services); 15. Higher high school enrollment. Factors 8, 9, and 10 show that when other variables are held constant, giving is positively correlated with need. Iri other words, adequate recog nition of need on the part of the people is very important for the enhancement of giving. Conclusions Several conclusions are supported on the basis of the statistical analysis of the data. These conclusions are valid, however, only for the samples and techniques employed in this study. 1. It is possible to construct reasonably accurate, efficient, and stable instruments, according to the multiple linear regression, Glueck, predictive configuration and Burgess Methods, to predict such a community variable as per capita giving. 190 2. Instruments based on the multiple linear regression method can predict a quantitative variable in term s of actual quantities or amounts, whereas others can be used to predict if the criterion is in a dichotomous form, 3. Instruments based on the method of multiple linear re gression are, for the most part, more accurate and efficient than those based on the other methods, Glueck-type instruments are the second best, 4, The instruments discriminate among the individual cate gories of the criterion with a reasonable degree of accuracy, though most predict the "high" category more accurately than the "low, " 5, The instruments based on the various methods possess a high degree of stability. Multiple linear regression instruments are not as stable as some of the others. Further Research Needed This research has demonstrated that accurate, efficient, and stable instruments for predicting a community variable, namely, per capita giving, can be constructed according to the commonly used methods of social prediction. The writer feels that these prediction methods might be employed for developing instruments to predict other community criteria, provided, of 191 course, adequate data are uniformly available on a sufficient num ber of factors related to the criterion. Some examples of community performance or behavior for which prediction instruments might be employed are these: 1, To predict the potentialities for the success or failure on the part of a number of communities to accomplish a communi ty project of a similar nature, 2. To predict how certain communities would vote on a particular issue, uniformly applicable to a sufficiently large number of communities. Prediction has immense potentialities for gaining scientific and tested knowledge concerning a community phenomenon. The development of prediction is, indeed, a sine qua non for the ad vancement and growth of both theory and practice. In fact if community organization or community development is to make a real headway, and if its methods are to become more scientific, it is essential that more studies, involving prediction of community characteristics or phenomena, should be undertaken, A brief account of some major shortcomings of the present study might serve some useful purpose. Some of the most signi ficant data, including such information as the number of campaign volunteers, proportion of non-firm gifts raised by payroll deduction, were based largely on rough estimates. 192 The writer feels that there are various factors other than those used that might have significant influence on giving, but, since it was not feasible to obtain, uniformly reliable data for them they had to be ignored. Some of these were quality and nature of public relations; type of the slogans used; popularity and competence of the campaign chairman; executive and organi zational abilities of the professional and voluntary leaders; nature of the weather during or immediately preceding the campaign; active interest or the lack of it on the part of the management of a major firm in the community; the nature of cooperativeness and involvement on the part of the local labor and other leaders; the extent and scope of the social welfare needs and the degree to which these have acquired the status of "felt needs"; popularity or unpopularity of one or more member agencies; absence or presence of some natural catastrophe, and so on. Any one of these factors by itself or a combination or permutation of these might have the potentiality of enhancing or reducing the over-all performance sufficient to invalidate the outcome predicted for a community. Although some of these factors might be regarded as more or less "intangibles" for the present, it is quite probable to develop reasonably accurate indices for a number of them, and thus it might become feasible to take these into account while constructing an instrument. 193 It would certainly be helpful for future studies concerning a community phenomenon if agencies in the field of community organization or development, such as the United Community Funds and Councils of America, Incorporated, make a greater effort to gather data uniformly from their member or affiliated agencies. It is recommended that for future prediction studies involv ing a community phenomenon, sequential-type instruments might be developed. With such instruments it is possible to revise the initial prediction by taking into account the intervening factors that might have some effect on the outcome. 194 APPENDIX A Names of Communities Included in the Study, by State Those Used in the Construction of Instruments Based on 1950 Data (N=350) Alabama; Birmingham, Florence, Gadsden, Huntsville, Mobile, Montgomery, Selma, Tuscaloosa. Arizona: Phoenix, Tucson, Yuma. Arkansas: Fort Smith. California: Alameda, Alhambra, Burbank, Burlingame, Glendale, Long Beach, Palo Alto, Pasadena, Pomona, Riverside, Sacramento, San Diego, San Francisco, San Rafael, Santa Ana, Santa Barbara, Santa Monica, Santa Paula. Colorado; Colorado Springs, Denver, Pueblo. Connecticut: B ristol, Meridan, Middletown, New Britain, Stamford, Torrington, Waterbury, Delaware; Wilmington. Florida: Daytona Beach, Fort Lauderdale, Jacksonville, Lakeland, Miami, Panama City, St. Petersburg, Tampa, West Palm Beach. Georgia: Atlanta, Athens, Columbus, Gainesville, La Grange, Newnan, Rome, Savannah. Illinois: Alton, Aurora, Berwyn, Bloomington, Champaign, Chicago, Cicero, Elgin, Evanston, Galesburg, Kankakee, Moline, Mt. Vernon, Oak Park and River Forest, Peoria, Quincy, Rockford, Springfield, W aukegan. Indiana: Bloomington, Elkhart, Evansville, Fort Wayne, Gary, Hammond, Indianapolis, Kokoma, Marion, Michigan City, Mishawaka, Muncie, New Albany, Richmond, Shelbyville, South Bend, Terre Haute. 195 Iowa: Burlington, Cedar Rapids, Clinton, Council Bluffs, Davenport, Des Moines, Dubuque, Port Dodge, Iowa City, Mason City, Ottumwa, Sioux City, Waterloo, Kansas: Hutchinson, Kansas City, Topeka, Wichita. . __ _ Kentucky: Frankfort, Henderson, Lexington, Louisville, Owensboro, Peducah. Louisiana: Baton Rouge, Lafayette, New Orleans, Shreveport. Maine: Bangor. Maryland: Anapolis, Baltimore, Cumberland, Hagerstown, Ferryville. Massachusetts: Beverly, Boston, Chicopee, Fall River, Fitchburg, H averhill, Holyoke, Lawrence, Northampton, P ittsfield, Quincy, Tauton, Worcester. Michigan: Alma, Ann Arbor, Battle Creek, Bay City, Caro, D etroit, Grand Rapids, Jackson, Kalamazoo, Lansing, Monroe, Mt. Clemens, Muskegon, Pontiac, Port Huron, S ag in aw . Minnesota: Albert Lea, Duluth, Fairmont, Minneapolis, Rochester, St. Cloud, St. Paul, Stillw ater, Willmar, W inona. M ississippi: Greenville, Jackson. M issouri: Columbia, Jefferson City, Joplin, Kansas City, St. Joseph, St. Louis, Springfield. Montana: Great Falls. Nebraska: Lincoln, Omaha Nevada: Reno New Hampshire: Manchester, Nashua. New Jersey: Atlantic City, Bayonne, B elleville, Bloomfield, Camden, Hackensack, Jersey City, M ontclair, Morris town, Newark, Nutley, Plainsfield, New Brunswick, P a s s a ic . New Mexico: Albuqurque, Roswell. 196 New York: Albany, Amsterdam, Auburn, Batavia, Binghamton, Buffalo, Cortland, Elmira, Ithaca, Jamestown, Lock- port, Mount Vernon, New Rochelle, Niagara Palls, Poughkeepsie, Rochester, Rome, Syracuse, Troy, Utica, Watertown, Yonkers. North Carolina: Asheville, Charlotte, Durham, Fayetteville, High Point, Raleigh, Rocky Mount, Wilmington, Winston-Salem. North Dakota: Fargo, Grand Forks. Ohio: Akron, Alliance, Barberton, Canton, Cincinnati, Cleveland, Columbus, Dayton, Hamilton, Ironton, Lancaster, Lima, Lorain, Mansfield, Marion, M assillon, Middletown, Newark, Portsmouth, Sandusky, Sidney, Springfield, Steubenville, Toledo, Urbana, Warren, Zanesville. Oklahoma: Enid, Oklahoma City, Tulsa. Oregon: Astoria, Eugene, Medford, Portland, Salem. Pennsylvania: Allentown, Altoona, Erie, Lancaster, Lewistown New Castle, Philadelphia, Pittsburgh, Readkig, Scranton, Stroudsburg, Washington, W illiamsport. South Carolina: Charleston, Columbia, Greenville. Tennessee: Chattanooga, Elizabethton, Jackson, Johnson City, Knoxville, Memphis, Nashville. Texas: Abilene, Amarillo, Austin, Beaumont, Big Spring, Corpus Christi, Dallas, El Paso, Ft. Worth, Houston, Laredo, Lubbock, M arshall, Nacogdoches, Paris, San Angelo, San Antonio, Tyler, Waco, Wichita Falls. Utah: Ogden, Salt Lake City. Vermont: Burlington. Virginia: Charlottesville, Danville, Lynchburg, Newport News Norfolk, Petersburg, Portsmouth, Richmond, Roanoke, S ta u n to n . Washington: Bellingham, Bremerton, Everett, Port Angeles, Seattle, Spokane, Tacoma, Vancouver, Walla-Walla, Y akim a. West V irginia: Charleston, Fairmont, Huntington, Martinsburg, W h e elin g . Wisconsin: Appleton, Beloit, Eau Claire, Green Bay, Kenosha, La Crosse, Madison, Manitowoc, Milwaukee, Oshkosh, Racine, Sheboygen, Superior, Wausau. Those Used in the Construction of Instrum ents Based Qn 19&0 Data (N=100) Alabama: Birmingham, Tuscaloosa. Arizona: Phoenix. Arkansas: Port Smith, L ittle Rock. Connecticut: B ristol, Meriden, Stamford, Waterbury. Florida: Port Lauderdale. Georgia: Athens, Gainesville. Indiana: Elkhart, Port Wayne, Kokoma, Marion, South Bend. Iowa: Port Dodge, Mason City, Ottumwa, Waterloo. Kansas: Kansas City, Wichita. Kentucky: Frankfort, Lexington, Paducah. Louisiana: Alexandria, Lafayette, Shreveport. Maryland: Baltimore, Cumberland, Hagerstown. M assachusetts: Beverly, Brockton, Chicopee, Haverhill, Pittsfield. Michigan: Ann Arbor, Battle Creek, D etroit, Jackson, Kalamazoo, Muskegon, Saginaw. Minnesota: Minneapolis, Rochester. M ississippi: Jackson. Montana: Great Palls. Nebraska: Omaha. Nevadc Reno. New Hampshire: Manchester. North Carolina: Charlotte, Durham, Raleigh, Wilmington. 198 Ohio: Alliance* Cleveland, Hamilton, Lima, Mansfield, Massilon, Middletown, Newark, Springfield, Toledo, W arren . Oklahoma: Oklahoma City Oregon: Medford, Portland, Salem. Pennsylvania: Allentown, Johnstown, Lewistown, Reading, Sharon, Washington, W ilkes-Barre, W illiamsport. South Carolina: Greenville, Spartanburg. South Dakota: Rapid City. Tennessee: Nashville. Texas: Dallas, El Paso, Lubbock. Utah: Ogden, Salt Lake City. Vermont: Burlington. V irginia: Charlottesville, Danville, Portsmouth. Washington: Bellingham, Bremerton, Everett, Walla-Walla. West V irginia: Charleston, Fairmont. Wisconsin: Milwaukee, La Crosse, Green Bay. NOTE: Communities Included in Validation Samples: 1. 1951 Sample (N=200). Those among the 350 communities, used in the construction of instruments based on 1950 data, for whom completed questionnaires were received. 2. 1961 Sample I (N=100). Same as those used in the con struction of instruments based on i 960 d a t a . 3 . 1961 Sample II (N=50). See Table 6l . 199 APPENDIX B Operational Definitions of Variables Used For Instruments based on 1950 data 1. Population Size. The population of a community according to the 1951 Directory of the United Community Funds and Councils of America, Incorporated. 2. Median Family Income. Median Family Income, 1950. 3. Poverty Index. Per cent of families having incomes of less than $ 2,000 i n 1950. 4. W ell-to-do-families. Per cent of families having incomes of more than $ 5, 000, 1950. 5. Non-white Population. Per cent of the total number of Negroes, Indians, Japanese, Chinese and other non-white races to the total population, 1950. 5. In-migration.Pep cent of inhabitants one year old and over who were living in a different county or a b ro a d i n 19^ 9 * 7. Productive Population. The per cent of population 15-64 years old, 19$0. 8 * Craftsmen• Th® P®r cent of craftsmen, Foremen and Kindred Workers in the labor force, 1950. 9 . Unskilled Workers. The per cent of the employed poplation included in the following Census Categories: "Operatives and Kindred Workers," "Private Household Workers," "Service Workers, except private household," "Farm Laborers, except unpaid and farm foremen," and "Laborers, except farm and mine," 1950. 10i White Collar Workers. The percentage of total employed persons in the combined Census Categories of "Professional, Technical and Kindred Workers," "Managers, officials, and proprietors, except farm, Clerical and Kindred Workers," and "Sales Workers," 1950. 200 11. Unemployment Index. This was computed by sub tracting the number of total employed from the number of persons, 14 years and over, in the civilian labor force, 1930. 12. Health Index. Ratio of number of infants deaths to total number of live births, 1950. 13. Dwelling Conditions. Per cent of dwelling units having hot runningwater, private toilet and bath and not dilapidated, 1950. 14. Dwelling Modernity. The per cent of dwelling units with mechanical refrigeration. 13. High School or more Education. The per cent of persons 25 years old and over who completed high school or more, 1950. 16. Less than 5 Grades Education. The per cent of p e rs o n s 25 years old and over who completed less than 5 grades, 1950. 17- High School Enrolment. The per cent of person 14-17 years old enrolled in schools, 1950. 18 . Median School Years Completed. The median school years completed by persons 25 years old and over, 1950. 19- Industrial Index. Sura of per capita value added by m anufacture,per capita wholesale sales, per capita retail sales, per capita receipts from personal, business and repair services. 20. Campaign Achievement. Ratio of total amount raised to the g o al,1949* 2 1 . Net Effective Buying Income. Per capita annual income after subtraction of personal taxes, 1950. 2 2 . Population Increase. The per cent of population increase since 1940. 23- Women Employed. The per cent of females 14 years old and over in the labor force, 1950. 24. Geographical Location. The median family income of the region in which a community is located, 1950. 201 2 5 * Crime Index. Number of burglaries, reported to the police, per 10,000 population, 1950. 2 6 . Juvenile Delinquency. Number of auto thefts, reported to the police, per 1,000 persons 7-17 years old, 19S>0. 2 7 * Government Expenditure on Public Welfare. Per capita government expenditure on publicwelfare, 1 9 5 0. 2 8 . Government Expenditure on Health and Hospitals. Per capita government expenditure on Health and Hospitals, 1 9 5 0. 29. Government faxes. Per capita government taxes, 1 9 5 0. 3 0 . Voting Behavior. Ratio of the total number of persons who actually voted in 1952 Presidential election to the total population of voting age, 1 9 5 0. 3 1 . Social Welfare Workers. Number of social welfare workers per 10,000 population, 1950. 3 2 . Payroll Deduction. Proportion of non-firm gifts raised through payroll deductions, 1951* 33. Campaign Volunteers. Total number of volunteers, who worked for the campaign, per 10,000 population, 1 9 5 1. 3 4 . Campaign Budget. Per capita expenditure on cam paign and general administration, 1951* 35. Per capita Giving. Per capita amount raised by a community fund-raising federation, 1951* For Instruments Based on i960 Data 1 . Population Size. The population of a. community according to the 1961 Directory of the United Community Funds and Councils of America, Incorporated. 2 . Median Family Income. Median family income, i960. 3. Poverty Index. Per cent of families having incomes of less than $3,000 in i960. 202 4. W ell-to-do-families* Per cent of fam ilies having incomes of more than $ 10, 000, i 960. 5 . In-migration.Per cent of inhabitants five years old and over who were living in a different county or abroad during 1955 and i 960. 6 . White Collar Workers. Per cent of white collar w o rk e rs , I 960. 7. Unemployment Index. Per cent of persons 14 years old and over not "at work" but looking for work i 960 . 8 . Dwelling Conditions. Per cent of dwellings not dilapidated, and"with sound fixtures, i 9 60 . 9 . High School Enrolment. The per cent of persons 14-17 years old enrolled in schools, i 960 . 10. Median School Years Completed. The median school years completed by persons 25 years old and over, I960. 11. Industrial Index, i 960. (Similar definition as f o r 1 9 5 6 .) ------ 12. Campaign Achievement. Ratio of total amount raised to the goal, 1959* 13. Net Effective Buying Income, i 960. (Similar definition as for 195^ . ) 14. Population Increase. The per cent of population increase since 1950. 1 5 . Women Q nployed, i 960. (Similar definition as for 1 9 5 0 .) 16. Geographical Location, i 960. (Similar definition as for 1950.) 17. Payroll Deduction, i 960. (Similar definition as fo r 1$5'0'T) ------ 18. Campaign Volunteers, 1961. (Similar definition a s f o r I 95O.) 203 19* Campaign Budget, 1961. (Similar definition as for 1 9 5 0 .) 20. Per capita Giving, 1961. (Similar definition as For” 195'0. J" — 204 APPENDIX C Sources of Data For Instruments Based on 1950 Data Sources Variables* Governmental Affairs Institute, America Votes, Vol. I, 1956 30 Sales Management, Survey of Buying Power, May, 1951* 21 The Respective Fund-raising Federations 32, 33* 34 U.S. Bureau of the Census, County 1,2,3*4,5,6,12 And City Data Book (1952), 13*14,15*16,17* Tables 1, 3 and 4. 18,19,22,23*24, 27,28,29*31 U.S. Breau of the Census, 7*8,9,10,11 U.S. Census of Population: 1950, Vol. II, Characteristics of the Population, Tables 33, 35, 41, 43* 73 and 75* United Community Funds and Councils of 1, 20, 35 America, Incorporated, 1950 and 1951 Directory U.S. Federal Bureau of Investigation, 25, 26 Uniform Crime Reports for the United States and its Possessions, 1951 For Instruments Based on i 960 D ata Sales Management, Survey of Buying Power, May, 1961. 13 *The numbers assigned to the variables are the same as in Appendix B. 205 Sources V a ria b les The Respective Fund-raising Federations 1 7 ,1 8 ,1 9 United Community Funds and Councils of America, Incporated, i 960 and 1961 Directory 1, 12, 20 U.S. Bureau «f the Census, U.S. Census 1, 2, 3, 4, 5, 6, 7, of Population: i 960, Vol. I, General 9,10,14,15,16 Social and Economic C haracteristics, 1961, Tables 32, 33, 35, 36. U.S. Bureau of the Census, U.S. Census of Business, 1958, Vol. II, Retail Trade, Area Statistics, Parts 1 and 11 2, 1961, Tables 102, 103. U.S. Bureau of the Census, U.S. Census of Business, 1958, Vol. IV, Wholesale Trade, Area Statistics, 1961, 11 Tables 102, 103. U.S. Bureau of the Census, U.S. Census of Business, 1958, Vol. VI, Selected 11 Services, Area Statistics, Parts 1 and 2, 1961, Tables 102, 103. U.S. Bureau of the Census, U.S. Census of M anufacturea,, 1958, Vol. Ill, Area 11 Statistics, 1961, T ab le 3* U.S. Bureau of the Census, U.S. Census of H o u sin g , i 960, Vol. I, States and 8 '- Small Areas, 1961, Table I. 206 APPENDIX D QUESTIONNAIRE DATA SHEET8, Campaign Prediction Project 1 . C ity : ______S ta te 2 . Name o f f e d e r a t e d cam paign f o r b 1 9 5 1 :______1 9 6 1 : ______ 3. Was American Red Cross included for 1951: Yes ; No: ____ 1961: Yes____ ; No: _____ 4. Proportion of non-firm giftsraised by payroll deduction. (Check one figure for each year. Use estimates if necessary.) 1951 1961 None ______ Some, less than 25#______ 25# to 49#______50# to 74#______ 75# or more______5. Total number of volunteers for the campaign held for 1951: ^______1961: (number) (number) aPlease return to Research and Statistics Division, United Community Funds and Councils of America, 345 East 46th Street, New York 17, New York, by January 31, 1962 (earlier if possible). bWhere 1951 or 1961 is used, reference is to the cam paign to raise funds for that year, usually held in the fall of the preceding year. 207 6. Total budget for campaign and UP or Chest general admin istration (Exclude Community Welfare Council, Social Service Exchange and sim ilar central services.) for the year in which the campaign was held for: 1951: $ ______; 1961: $ ______7. Identify the area (cities, counties or other political subdivisions) included in your campaigns for 1951 and 1961 (in such a way that publications of the Bureau of the Census can be used to determine population and other characteristics of your campaign area). 1 9 5 1 :______ 1961: 208 BIBLIOGRAPHY Public Documents U.S. Bureau of the Census. U.S. Census of Population., 1950, Vol. II, Characteristics of the Population. ______. County and City Data Book, 1952. ______. U.S. Census of Population, I960, Vol. I, General Social and Economic Characteristics, 1961. ______. U.S. Census of Housing, i 960, Vol. I, States and Small Areas, 1961. U.S. Census of Business, 1958, Vols. II, IV and VI, 1961. . U.S. Census of Manufacturers, 1958, Vol. Ill, 1961. U.S. Federal Bureau of Investigation, Uniform Crime Reports, 1951. Books Andrews, Emerson F. Philanthropic Giving. New York.1:: Russell Sage Foundation, 1950. Corporation Giving. New York: Russell Sage Founda- tion, 1952. ______Attitudes Toward Giving. New York: Russell Sage Foundation, 1953* Angell, Robert C. The Moral Integration of American C ities. Chicago: The University of Chicago Press, 1951* Baylor, Edith, and Monachesi, E. D. 'pie R ehabilitation of Children. New York: Harper and Bros., 1939* Church, David M. Philanthropic Fund Raising as a Profession. Cambridge, Massachusetts: Bellman Publishing Co., 1957. 209 Dunham, Arthur, Community Welfare Organization. New York: Thomas Y. Cromwell Co., 1956. Ezekiel, Mordecai, and Pox, Karl A. Methods of Correlation and Regress ion Analysis. New York: John Wiley and Sons, Inc., 1959* Glueck, Sheldon, Glueck, Eleanor. 500 Criminal Careers. New York: Alfred A. Knopf, 1939* Five H undred D e lin q u e n t Women. New Y o rk : A lf re d A. Knopf, 1934. ______. Predicting Delinquenoy and Crime. Cambridge, Massachusetts: Harvard University tress, 1959* ______. Unraveling Juvenile Delinquency. New York: The Commonwealth Fund, 1950* Jonassen, Christian T., Peres, S. H. Interrelationships of Dimensions of Community Systems" Columbus: Ohio Monachesi, Elio D. Prediction Factors in Probation. Hanover The Sociological Press, 1932. Norton, William J. The Cooperative Movement in Social Work. New York: Macmillan Co., 1927• Peters, Charles C., and Van Voorhis, Walter R. S tatistical Procedures and Their Mathematical Bases. New York: McGraw-Hill Book Co., 19^0. KSeichenbach, Hans. Experience and Prediction. Chicago: University of Chicago Press, 1936. Shapiro, Company Giving. Chicago: Survey Press, i960. Stouffer, Samuel, e ta l. Measurement and Prediction. Princeton: Princeton University Press, 1950. Thorndike, E. L. Your City. New York: Harcourt Brace and C o ., 1939. ______. 144 Smaller C ities. New York: Harcourt Brace and Co., 1940. Void, G. B. Prediction Methods and Parole. Hanover, N.H.: The Sociological Press, 1931* Yule, G. Udny, and Kendall, M. G. An Introduction to the Theory of Statistics, 11th eTI London: Charles G 'r " i f f l n and Co., 1937. 21Q Artbles and Periodicals Burgess, Ernest W. "is Prediction Feasible in Social Work," Social Forces, 7:533-45, 1929- ______. "Factors Determining Success on Parole," Journal of Criminal Law and Criminology, XIX, 1928, 239-366. Karter, Thomas. "Voluntary Agency Expenditures for Health and Welfare from Philanthropic Contributions, 1930- 55," Social Security B ulletin, XXI, February, 1958. Lilley, S. "Can Prediction Become a Science," Discovery, 7:336-40, 1946. Monachesi, Elio D. "An Evaluation of Recent Major Efforts at Prediction," American Sociological Review, 6:478- 86, 1941. ------ Ohlin, Lloyd E., and Duncan, Otis D. "The Efficiency of Prediction in Chriminology," American Journal of Sociology, LIV (1949). 441-45^ - Page, David P. "Measurement and Prediction of Leadership," American Journal of Sociology, 41:31-43, 1935* Patterson, C. H. "On the Problem of the Criterion in Predic tion Studies," Journal of Consulting Psychology, Vol. X, pp. 277-280,'T946: Reckless, Walter C. "The Implications of Prediction in Sociology," American Sociological Review, 6:471-77, 1941. Reiss, Albert J. "The Accuracy, Efficiency, and Validity of a Prediction Instrum ent," American Journal of Sociology, LVI (1951), 552-56T: Roterus, Victor, "Effects of Population Growth and Non Growth on the Well-Being of C ities," American Socio logical Review, 11:90-97, 1946. Sales Management. Survey of Buying Power, May, 1951* ______. Survey of Buying Power, Bay, 1961. Tibbetts, Clark. "Success or Failure on Parole Can be Pre dicted: A Study of 3,000 Youths Paroled from the Illinois State Reformatory," Journal of Criminal Law and Criminology, XXII, 1931, 11-50. 211 Pamphlets and Bulletins Community Chests and Councils, Incorporated. Yesterday and Today with Community Chests. New York: 1937* _ . Organizing and Operating a Community Chest, Bulle- 143, R ev. edT New Y ork: 1952. Organizing a United Fund,Bulletin 165, New York: 195T r United Community Funds and Councils of America, Incorpor- ated. A Method for Measuring Fund Raising Potential, New Y ork: 1958* _ . The Syracuse Corporate Yardstick, New York: i 960 . R e p o rts United Community Funds and Councils of America, Incorpor a t e d . 1950 D i r e c t o r y , New Y ork, 1 9 5 0. _ . 1951 Directory, New York, 1951. _ . i 960 Directory, New York, i 960. . 1961 Directory, New York, 1961. Unpublished M aterial Stuckert, Robert P. "A Configurational Approach to Social Prediction." Unpublished Doctoral dissertation, Department of Sociology and Anthropology, The Ohio State University, 1956* Other Sources American Cancer Society. News Release, March 17* 1955* United Community Funds and Councils of America, Incorporated. Personal interviews with the Director and other mem bers of the Research Department. United Appeal of Franklin County, Inc. Personal interviews with the Executive Director and other members of the s t a f f . United Community Council, Inc. Personal Interview with the Head of the Research Department. 212 AUTOBIOGRAPHY I, Amina Kumar Singh Yadava, was born in Village Palra, D istrict Gurgaon, Punjab, August 19, 1921. I attended schools at Gurgaon, Datia, and Delhi. In 19^0 I received the Bachelor of Science degree from St. Stephen's College, Delhi; my major subjects were physics and mathe matics. I continued my studies at Ramjas College, Delhi, and graduated with a Master of Arts degree in History in 1942. Afterwards, I did voluntary social work in my village for ten years. I helped to organize "Gram Sewa Saraaj" (Village Service League), which carried on various social welfare activities. In 1952, after graduating with a Bachelor's degree in Law, I joined the Delhi School of Social Work, from which I graduated with a M aster's degree in Social Work in 195^. Prom 1955 to 1957 I worked with the Bharat Sewak Samaj in the capacities of Associate Director, Bherat Sewak Semaj Community Organizers Training Center, and Zonal Organizer for the States of U.P. and M.P. In 1957 I joined the Delhi School of Social Work as a lecturer and field work supervisor and worked there until 19^9* A scholar ship grant from Indiana University in 1959 enabled me to come to the United States for further studies in Social Work. From this university I received an A.M. degree in Social 213 Group Work in i 9 6 0 . For the next two years I worked as a graduate research assistant at the Ohio State University and during this period I completed the requirements for the Doctor of Philosophy degree. 214